(SpringerBriefs in Education) Allison Littlejohn, Nina Hood - Reconceptualising Learning in The Digital Age-Springer Singapore (2018)

SPRINGER BRIEFS IN
OPEN AND DISTANCE EDUC ATION
Allison Littlejohn
Nina Hood
Reconceptualising
Learning in the
Digital Age
The [Un]democratising
Potential of MOOCs
SpringerBriefs in Education
Open and Distance Education
Series editors
Insung Jung, International Christian University, Mitaka, Tokyo, Japan
Colin Latchem, Perth, WA, Australia
More information about this series at http://www.springer.com/series/15238
Allison Littlejohn Nina Hood
•
Reconceptualising Learning
in the Digital Age
The [Un]democratising Potential of MOOCs
123
Allison Littlejohn Nina Hood
Open University University of Auckland
Milton Keynes Auckland
UK New Zealand
ISSN 2211-1921 ISSN 2211-193X (electronic)

SpringerBriefs in Education
ISSN 2509-4335 ISSN 2509-4343 (electronic)
SpringerBriefs in Open and Distance Education
ISBN 978-981-10-8892-6 ISBN 978-981-10-8893-3 (eBook)
https://doi.org/10.1007/978-981-10-8893-3
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Contents
1 The Many Guises of MOOCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1 Introducing MOOCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 MOOC Dimensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 The Origins of MOOCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.4 Conceptualising What It Means to Be MOOC . . . . . . . . . . . . . . 6
1.5 Shifting Meanings: What Do Massive, Open, Online
and Course Really Mean? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.6 Massive . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.7 Open . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.8 Online . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.9 Course . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.10 MOOC Ideologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.11 The Ambitions of This Book . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2 The [Un]Democratisation of Education and Learning . . . . ........ 21
2.1 The Hype, De-hype and Re-hype of MOOCs . . . . . . . ........ 21
2.2 The Learnification of Education; the Wider Context of
MOOCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.3 Towards Democracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.4 Different Challenges, Same Outcome . . . . . . . . . . . . . . . . . . . . . 30
2.5 New Name, Repeating Model . . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.6 Concluding Thoughts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3 The Emancipated Learner? The Tensions Facing Learners
in Massive, Open Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.1 Individual Learner, Common Challenges . . . . . . . . . . . . . . . . . . 35
3.2 Student, Learner, User, Participant—Multiple Names
for Multiple Actors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
v
vi Contents
3.2.1 The Student, the Learner . . . . . . . . . . . . . . . . . . . . .... 37

3.2.2 The User, the Participant . . . . . . . . . . . . . . . . . . . . .... 39
3.3 Why a MOOC? Motivations and Incentives Among MOOC
Learners . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .... 41
3.4 But Who Benefits? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .... 47
3.5 A Closer Look at the Role of Self-regulated Learning in
MOOCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.6 Learning Behaviour: Diversity in Engagement . . . . . . . . . . . . . . 50
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
4 Massive Numbers, Diverse Learning . . . . . . . . . . . . . . . . . . . . . . . . . 57
4.1 Learning in MOOCs; What Does It Mean? . . . . . . . . . . . . . . . . 57
4.2 Individual-Level Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
4.3 The Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
4.4 Analysing the Norms of Behaviour . . . . . . . . . . . . . . . . . . . . . . 61
4.5 Qualitative Narratives and Learners’ Stories . . . . . . . . . . . . . . . . 65
4.6 Making Sense of the Learner Stories . . . . . . . . . . . . . . . . . . . . . 70
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
5 Designing for Quality? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
5.1 Contested Purpose, Uncertain Quality . . . . . . . . . . . . . . . . . . . . 79
5.2 Notions of Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
5.3 Quality of Platform Provider . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
5.4 Quality of Instructor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
5.5 Quality of Learning Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
5.6 Quality of Adaptability to Context . . . . . . . . . . . . . . . . . . . . . . . 88
5.7 Quality of Outcome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
6 A Crisis of Identity? Contradictions and New Opportunities . . . . . . 95
6.1 When Actions Contradict Aims . . . . . . . . . . . . . . . . . . . . . . . . . 95
6.2 Restraining Elitism, Embracing Democracy . . . . . . . . . . . . . . . . 96
6.3 MOOCs as a Disrupting, not Reinforcing, Influence . . . . . . . . . . 98
6.4 Opportunities for All: Supporting Self-regulation . . . . . . . . . . . . 102
6.5 Rethinking Success Measures . . . . . . . . . . . . . . . . . . . . . . . . . . 103
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
Summary and Overview
Summary: Massive open online courses (MOOCs) have been signalled as a dis-
ruptive and democratising force in education. This book examines these claims,
identifying characteristics that influence their development: MOOCs appear to
advantage the elite, rather than act as an equaliser; they tend to reproduce formal
education, rather than disrupt it; they are designed for those who can learn, rather
than opening access for all; and they are measured by metrics that may not be
appropriate for open, distance education. These tensions are analysed and potential
ways forward are sketched out.
Overview: Massive open online courses have become popular in recent years. The
term MOOC has become synonymous with almost any open, online learning. This
book identifies specific tensions that exemplify MOOCs and characterise open,
online learning in general:
• MOOCs have the potential to democratise education. However, by highlighting
prominent universities and organisations, they reinforce the values and extend
the influence of the privileged. Open, online learning could be introduced in
ways that emphasise the value, knowledge and cultures of all societies and
institutes.
• MOOCs have the potential to disrupt education. Yet, rather than being based on
a future-focused view of learning, MOOCs often are modelled on the designs
and traditions of conventional education. These norms include an expectation
that learners intend to complete a course or that they will complete assignments,
yet research illustrates that MOOC learners often have very different intentions.
MOOC designs could be future-focused to ensure they disrupt education, rather
than replicate conventional forms of learning online.
• An important feature of MOOCs is to open access to learning for everyone.
Conversely, they are designed in ways that require learners to regulate their own
learning even though there is ample research that indicates not everyone has the
capability to learn independently. More emphasis should be placed on gov-
ernments to make sure all citizens have the ability to regulate their learning.
vii
viii Summary and Overview
Until this happens, all forms of open, online learning will benefit those who can
learn, rather than serving everyone.
• A vision that underscores open, online learning is that learners can follow their
own goals. Yet MOOC designs and analytics often are based on predetermined
objectives, rather than learner-defined goals. Learners usually are expected to
conform to expected ‘norms’, such as submitting an assignment or completing a
course. MOOCs could be designed in ways that allow learners’ autonomy and
freedom to learn what they want in ways that suit them.
• An important aspect of the vision of people learning autonomously in MOOCs
is the idea of drawing on the support of the massive numbers of other learners in
the MOOC. Yet these social features of MOOCs often are missing. MOOCs
have to be designed to allow learner interaction with other learners and with
tutors.
• Data that are used to measure progress in open, online platforms may provide a
reductionist view of learner development. Future analytics platforms and tools
for open, online learning should capture data in ways that provide a holistic
understanding of the learners’ intentions and scaffolds to support them in
achieving their goals.
• Open, online courses and credentials sometimes are viewed as products for
‘consumer’ students. This view might oversimplify the notion of learning as a
means to transform human thinking and practice. This transformative role of
education and learning has to underpin our future planning and policy around
open, online learning.
Chapter 1
The Many Guises of MOOCs
Abstract Massive open online courses (MOOCs) often are viewed as synonymous
with innovation and openness. In this chapter, we trace their origins and varied man-
ifestations and the ways they are understood. We interrogate the wide-ranging uses
and interpretations of the terms massive, open and course, and how these terms
are represented in different types of MOOCs. We then identify contradictions asso-
ciated with MOOC excitement. Despite the initial agenda of MOOCs to open up
access to education, it is seen that they tend to attract people with university edu-
cation. Rather than offering scaffolds that support people who are not able to act
as autonomous learners, MOOCs often are designed to be used by people who are
already able to learn. Like traditional education systems, MOOCs usually require
learners to conform to expected norms, rather than freeing learners to chart their own
pathways. These norms sustain the traditional hierarchy between the expert teacher
and novice learner (Ross et al. 2014). A particularly troubling feature of MOOCs is
that, as supports are becoming automated and technology-based, this power structure
is becoming less visible, since it is embedded within the algorithms and analytics
that underpin MOOCs.
1.1 Introducing MOOCs
For many readers, MOOCs—massive, open, online courses—need no introduction.

The term is generally associated with innovation, openness and democratisation of
learning. The term ‘MOOC’ was first coined in 2008 when it was used to describe the
‘Connectivism and Connective Knowledge (CCK08)’ course offered by the Univer-
sity of Manitoba in Canada, which attracted over 2200 participants globally (Mack-
ness et al. 2010). The term had entered common parlance by 2012. Indeed, such was
the hype around MOOCs that The New York Times pronounced 2012 as ‘The Year
of the MOOC’.
The excitement surrounding MOOCs is in their potential to open up access to
education and allow millions of people around the world to engage in learning. The
original idea was that learners could choose how they want to learn and decide their
own learning outcomes. Learning is scaffolded by experts, by fellow MOOC learners,
© The Author(s) 2018 1
A. Littlejohn and N. Hood, Reconceptualising Learning in the Digital Age,
SpringerBriefs in Open and Distance Education,
https://doi.org/10.1007/978-981-10-8893-3_1
2 1 The Many Guises of MOOCs
by digital content and analytics-based systems, blurring the distinction between the
teacher and the learner and between the human and technology-based supports.
MOOCs have become an industry in their own right. Organisations have been
founded to offer MOOCs to millions of learners worldwide. ClassCentral,1 a website
aggregating data and information on MOOCs, listed 30 MOOC providers in 2017.
These providers partner with over 700 universities around the world to offer MOOCs.
It is estimated that around 58 million students had signed up for at least one MOOC
by the end of 2016, with 23 million registering in an MOOC for the first time that
year (Shah 2016). It is important to note here that these colossal numbers do not
support any specific understanding about the outcomes of these people who signed
up for these MOOCs. There is no discussion as to whether the 23 million refers to
discrete individuals or 1 million individuals each signing up for 23 MOOCs. Nor is
there evidence around how many of the people who enrolled actually participated or
learned in each MOOC. Yet, even around 2008–2012, when the evidence of whether
and how participants learn in an MOOC was limited, there was large-scale investment
in platform and course development.
Despite the phenomenal growth in MOOC numbers and participants, MOOCs are
somewhat inconsistent in how they are defined and changeable in the ways they are
realised, as will become clear over the course of this book. Over the past 6 years, their
purpose, forms and modes of operation have shifted to the extent that the suitability
of the acronym is now questionable.
The intention of this book is to examine claims that MOOCs have a disruptive and
democratising influence over higher education. However, the effects of MOOCs on
education are not as straightforward as they might seem at first glance. An analysis
of the literature points to a number of tensions that characterise MOOCs. First, they
appear to advantage the [learning] elite, rather than acting as an equaliser. Second,
they tend to reproduce traditional formal education, rather than disrupt these. Third,
they often are designed for those who can learn, rather than opening access for those
who cannot. Fourth, even when learners have the ability to learn autonomously, they
often are expected to conform to course norms, rather than determining their own
learning strategies and pathways. Fifth, MOOCs are conceived as social networks
that allow learners to learn through dialogue with others. MOOCs also tend to be
regulated by algorithms and metrics that are based on conventional education, rather
than on future-facing forms of learning and these may not be appropriate for open,
distance education. Finally, the view of MOOCs as a product for the consumer
learner may overly simplify the complex, transformational processes that underscore
learning. Over the next five chapters, we describe these tensions and their impact on
education. These tensions also underpin in countless areas of open, online learning,
so the analysis in this book is applicable to a much wider context of open, online
learning than MOOCs. Many of the issues raised in this book are not restricted to
MOOCS and have much wider applicability.
We begin with an overview of the rudimentary precepts that define MOOCs and
to examine their historical origins in distance learning initiatives and more recently
online learning.
1 ClassCentral https://www.class-central.com/.
1.2 MOOC Dimensions 3
1.2 MOOC Dimensions
The words that make up the acronym MOOC highlight the fundamental, or at least
initially intended, dimensions of an MOOC; that is, they are online courses that
facilitate open access to learning at scale (McAuley et al. 2010). MOOCs, at least
theoretically, allow anyone with a device and Internet connection, no matter his or
her background, prior experience or current context, to access learning opportunities
free of charge. The learning experience of an MOOC is designed to provide learners
with the flexibility and freedom to chart their own learning journey and to engage in
ways that best enable them to reach their personally determined goals. However, the
interpretation and employment of the four dimensions of the acronym are not con-
sistent, resulting in considerable variation in purpose, design, learning opportunities
and access among different MOOC providers and individual MOOCs. Indeed, their
variable employment is influenced and shaped by the different forces and contexts
that are shaping MOOCs and changing paradigms and approaches in education in
learning. A theme that will be returned to in this chapter and throughout the book.
MOOCs are diversifying. There is increasing diversity both in the variation of
MOOC platforms and in the types of learning opportunities on offer (Anderson
2013). The original MOOCs were developed by educationalists using rudimentary
tools and platforms (Milligan et al. 2013). These MOOCs were funded through small-
scale projects and often staffed by educators volunteering their time and labour. The
leap from informal business arrangements to larger scale commercial enterprises took
place around 2011–12 when three US-based platform providers opened up: Udacity (
www.udacity.com), formed as a for-profit educational organisation, Coursera (www.
coursera.com), a spin-out from Stanford University and edX, funded by Harvard
University and Massachusetts Institute of Technology (MIT). The UK Government,
keen to be seen at the forefront of online learning innovation, founded FutureLearn
(www.futureLearn.com) in December 2012, as a for-profit company wholly owned
by The Open University.
Since these early platforms were introduced, a variety of online learning providers
have turned their attention to MOOCs as the ‘next big thing’, offering opportunity
for pioneering ventures, including the Europe-based Iversity (iversity.org) and Aus-
tralasian platforms Open2Study and OpenLearning. Non-Western MOOC providers
are growing in dominance, with the China-based XuetangX (www.xuetangx.com/
global) now the third largest MOOC provider by registered users.
MOOCs are viewed as a blossoming industry. However, despite the millions of
learners participating, it has been challenging to identify robust business models to
fund MOOCs, particularly when courses are offered free of charge to learners. An
early commercial model was based around partnerships with universities and other
organisations providing course materials and funding MOOC platform providers to
run each MOOC. However, this is expensive for universities and the return on invest-
ment is difficult to calculate. Therefore, after an initial rush to be seen to be producing
and running MOOCs, some universities began to scale back their investment, possi-
bly because of the limited evidence of return on investment.
New commercial models have been introduced. An increasing number of MOOCs

now have credentials and certification as a way to generate income. MOOC learners
learn for free but pay a premium for a course certificate. The US MOOC provider
Coursera is a leader in this form of income generation. Coursera introduced a ‘Sig-
nature Track’ in 2013, where learners who completed a course were offered an
assessment and the possibility of a course certificate for a fee of $49 (USD). It has
been estimated that the introduction of certificates generated $8–$12 million in rev-
enue for Coursera in 2014 (Shah 2014), though these figures are difficult to verify.
Coursera2 has since expanded this model as ‘Specialisations’, a sequence of four
to six MOOCs linked by a project or series of tasks that learners must complete
in order to earn a certificate. The fee for the certificate ranges from $300 to $600
(USD), depending on the number and cost of the constituent courses, generating the
potential for significant revenue.
US-based MOOC provider, Udacity uses a different model. Udacity offers fee-
based Nanodegrees, which in 2017 cost $200 per month over 10 months, with a total
cost to each learner of $2000 (USD). Udacity also offers college credit and degree
programmes. For example, a Masters in Computer Science is offered online through
a partnership with Georgia Tech. In 2017, 4000 students were enrolled in the Masters
course. Partnerships with universities offer platform providers opportunity to intro-
duce diverse ways to offer course credit, for example through formal accreditation
or micro-credentialing.
These examples illustrate how the economic pressures around who funds MOOCs
and how these are funded are pushing MOOC designs from their original position
of being open access and free of charge towards for-fee, closed, online courses that
mimic distance education courses offered by universities. Coursera, edX, Udacity
and FutureLearn all now offer courses that are only available to those who pay,
challenging notions that ‘openness’ means ‘no cost’ and ‘access for all’. The plat-
form providers argue that some courses provide a less expensive and more flexible
alternative to participating in campus-based degree courses. For example, from 2017,
FutureLearn and Deakin University offer full MOOC-based degree courses at a much
lower cost compared with studying full-time at Deakin.
Some MOOC platform providers are expanding their business by focusing on
the lucrative professional learning and business-to-business market, which has seen
MOOC providers partner with companies to create specific courses for their employ-
ees. The professional learning area offers the potential for new business streams. For
example, Coursera is experimenting with a revenue-generating recruiting service
which uses data analytics to connect students with ‘positions that match their skills
and interests’. Companies are charged a fee for an ‘introduction’ to a student and
the revenue is shared with the university offering the course. The MOOC platform
providers are likely to experiment with these and other analytics-based forms of
revenue generation to sustain their business.
2 How does Coursera Make Money. Blogpost available from: https://www.edsurge.com/news/2014-
10-15-how-does-coursera-make-money.
1.2 MOOC Dimensions 5
The practical reality of business models, and the balancing act between costs and
benefits that educational institutions have to perform to ensure MOOC sustainability
creates tension with the need to open up education to a larger number of learners
who need to learn continually throughout their lives. On the one hand, content and
accreditation increasingly are viewed by institutions as products that can be sold
to student consumers. Course products can be developed, offered and sold in an
accountable way. On the other hand, opening up learning requires MOOC participants
to behave as active learners. Making sure everyone is able to learn and measuring
whether they can is more difficult than simply selling products. Both these positions
are viewed as transformative, yet each requires a distinct plan of action. The simplicity
of creating and delivering course materials can be more alluring than the complex
process of making sure everyone can learn autonomously. There is a danger in overly
simplifying how we comprehend and measure ‘learning’, particularly if swathes of
the population are unable to take advantage of the new opportunities for learning
that MOOCs offer. However, education sectors have in the past focused effort on
advancing those who are already advantaged and MOOCs are rooted in the heritage
of education.
1.3 The Origins of MOOCs
MOOCs frequently are positioned as newcomers to, and potential game-changers in,
the education world. However, their origins may be traced back over one hundred
years to early distance learning enterprises, and more recently to the open education
initiatives which arose in the early 2000s. MOOCs have been positioned as hybrids
of previous attempts at online distance education, combining early approaches to
online distance learning with the scale and potential of open courseware and OER
(Gillani and Eynon 2014).
In many ways, MOOCs represent a fresh incarnation of distance learning, which
originated in the nineteenth century as correspondence courses using the postal sys-
tem, and later utilised radio and television broadcasts, and more recently online
learning. The first recorded instance of distance learning comes from Boston in
1728, when Caleb Phillips advertised private correspondence courses in the Boston
Gazette. Correspondence education then expanded extensively throughout the nine-
teenth century.
The University of London became the first university to offer distance learning
degrees in 1858, with several other universities, including the Universities of Oxford
and Cambridge in the United Kingdom and Illinois, Wesleyan University and the
University of Chicago, offering various extension services in the second half of
the nineteenth century. In 1969, the Open University, UK, became the first institu-
tion to deliver only distance learning—a model that soon spread to other countries,
including Canada, Spain, Germany and Hong Kong. The Open University also pio-
neered admission without qualifications and the concept of degrees awarded through
modular coursework. Students at the Open University engaged with a range of learn-
ing media, including specially produced textbooks, radio and later TV programmes
broadcast by the British Broadcasting Corporation (BBC), videotapes and in-time
computer-based learning.
The advent of the Internet enabled the development of new mechanisms for the
dissemination and transmission of content, as well as new open education oppor-
tunities, such as open courseware, and open educational resources (OER). In 2001,
MIT launched MIT OpenCourseWare, an initiative to put all its educational materials
from its undergraduate and postgraduate courses online, allowing anyone to access
and use the materials free of charge. OER similarly respond to notions of expiating
access to educational resources and knowledge. OER may be conceptualised as:
Digitised materials offered freely and openly for educators, students, and self-learners to use
and reuse for teaching, learning, and research. OER includes learning content, software tools
to develop, use, and distribute content, and implementation resources such as open licences.
(OECD 2007, p. 10)
The Cape Town Open Education Declaration (2008, available from http://www.
capetowndeclaration.org), a founding document of the OER movement, suggests
that open education has the potential to ‘empower educators to benefit from the best
ideas of their colleagues’ and to adopt ‘new approaches to assessment, accreditation
and collaborative learning’. While OER aim to open up access to information and
knowledge, a key criticism is that these resources tend to retain the idea of dissem-
inating and broadcasting information as text or video-based resources, rather than
drawing on the affordances of the Internet to support learning through active col-
laboration and knowledge building. This tendency to view educational resources as
information to be broadcast has expanded into MOOCs.
MOOCs have the potential to combine notions of distance learning initiatives with
open education opportunities, utilising the affordances of the Internet and digital
technologies to provide learning opportunities that are open to all, free of charge
and regardless of prior experiences and current context. As such, they represent a
continuation and combining of existing trends and practices in education. However,
the binary view of an MOOC, first as a set of content resources disseminated via
the Internet and, second, as an online space for learners to interact as they create
knowledge, makes it difficult to conceptualise what it means to be an MOOC.
1.4 Conceptualising What It Means to Be MOOC
The term MOOC is increasingly employed as a catchall phrase to denote a wide

range of online learning opportunities. The combinations of technology, pedagogical
frameworks and instructional designs vary considerably between individual MOOCs,
making it challenging to conceptualise exactly what is meant by the term. Early
MOOCs tended, with varying degrees of success, to reproduce offline models of
teaching and learning, focusing on the organisation, presentation and dissemination
of course material, while drawing on the Internet to open up these opportunities
1.4 Conceptualising What It Means to Be MOOC 7
to a wider audience (Margaryan et al. 2015). This model imitates earlier forms of
distance learning, where text-based or video-based course materials were distributed
to students using postal services. The idea here is that ‘learning’ (as a noun) comprises
materials that can be ‘delivered’ to students. Other models position ‘learning’ as a
verb. These models utilise the opportunities presented by the Internet and digital
technologies and combine these with new pedagogical approaches and the flexibility
of OER to design learning experiences where students actively engage in learning
activities. What is clear is that there is no single model for MOOC designs.
There have been numerous attempts to develop typologies of MOOCs (Depart-
ment for Business, Innovation and Skills 2013), and it increasingly is recognised
that any attempt at categorisation must embrace multiplicity, acknowledging the
diversity and often nuanced distinctions that can be made between MOOC designs,
purposes, pedagogical approaches and learners. There have been calls to abandon
the MOOC acronym altogether in favour of new titles, which more accurately cap-
ture the particular design and purposes of specific courses (Bayne and Ross 2014).
MOOCs have been described using a variety of different terms, including ‘DOCCs:
Distributed Open Collaborative Course’ (Jaschik 2013), ‘POOCs: Participatory Open
Online Course’ (Daniel 2012) and ‘BOOCS: Big (or Boutique) Open Online Course’
(Hickey 2013; Tattersall 2013). MOOCs are not always open and are sometimes avail-
able as ‘SPOCs: Small Private Online Course’ (Hashmi 2013) which may be closed
courses available for specific clients, such as corporate training for companies,
In other words, the term ‘MOOC’ is used to describe a wide range of different
types of online learning. The diversity of structure, purpose and designs of MOOCs
makes the term of limited use in indicating the educational and learning experiences
that MOOCs offer. As will be explored throughout this book, the specific nature and
composition of individual MOOCs are profoundly shaped and ultimately the product
of their platform and platform provider, designers and instructors, and the partici-
pants, who each bring their own frames of reference and contextual frameworks.
Furthermore, many of the ideas raised throughout this book in relation to learning,
the roles of learners and those responsible for designing and offering the learning,
and the structures governing MOOCs are relevant not just to MOOCs but also to
online education more generally.
While the concepts and discussion may broadly be relevant to many forms
of online education and learning, given that MOOCs serve as the case study for
exploring the concepts in this book, it is necessary to explore in greater detail the
complexities and variations in design and purpose in MOOCs. The following section
will unpack the ways in which the four dimensions of an MOOC—massive, open,
online and course—have been variously interpreted and implemented as well as
the various theoretical conceptions of MOOCs and how these shape perceptions of
their role, the nature of learning and the agency afforded to the different players
within them—learners, teachers or instructors, institutional providers, instructional
designers and the platforms themselves.
1.5 Shifting Meanings: What Do Massive, Open, Online

and Course Really Mean?
While the four words that make up the acronym MOOC collectively work to enhance a
democratising agenda, their meanings have become increasingly varied and in certain
cases distorted from their original intentions.
1.6 Massive
Massive typically is used in the context of MOOCs to reference the large number
of users who can participate in an MOOC. Early discussions of MOOCs focused on
the hundreds of thousands of learners signing up for a single MOOC. In this sense,
it is closely connected to notions of ‘open’ and the potential for anyone to access
learning opportunities.
The use of the term massive, and the extent to which it accurately represents
the reality of MOOCs, has been challenged on a number of grounds. Perhaps most
obviously, critics have challenged notions of massive given estimates that fewer than
10% of learners complete a course (Jordan 2015). This suggests that while MOOCs
can accommodate large numbers of learners, they have not yet managed to provide
consistently high-quality learning opportunities at this scale. Furthermore, the pre-
dominance of well-educated, males studying in MOOCs (Zhenghao et al. 2015) has
led to questioning around the ability of MOOCs to provide learning opportunities to
diverse participants or to truly open up access to education opportunities.
The large number of learners signing up for MOOCs prompts the questions:
What does it mean to provide learning on a mass scale? And which pedagogies are
effectively able to scale? (Downes 2013; Grover et al. 2013). Ferguson and Sharples
(2014, p. 98) suggest that to date ‘learning through mass public media is limited in its
effectiveness, and successful large-scale online education is expensive to produce and
deliver’. Establishing reliably sound pedagogical and instructional design models for
disseminating and facilitating learning opportunities at scale to potentially diverse
audiences remain elusive. Downes (2013) suggests that consideration must be given
not only to the question of content dissemination but also to support meaningful
interactions between learners.
Before the advent of MOOCs, Tyler (1993) warned that content ‘delivery’ cannot
exist in isolation from the activities that students engage within in order to learn.
Thus, the value of content is related only to the use and interpretation of content in
specific contexts. Selwyn (2016) has expanded on Tyler’s thesis to suggest that the
mass customisation of learning through large, digital systems has led to the primary
concern of how to deliver predetermined content to students, with often little ‘regard
to individuals’ relationships with others, and ‘the social and political contexts in
which they learn and act’ (p. 146). That is, MOOCs inadvertently have led to a
1.6 Massive 9
dehumanisation of teaching and learning and that their success is reliant on finding
a way to incorporate and ensure the human element.
This dehumanisation of the learning experience runs counter to the notion of
the learner at the centre and the learner determining what and how best they learn.
Research has consistently identified solely online learning to be less effective than
either blended or offline equivalents (Bettinger and Loeb 2017; Couch et al. 2014;
Figlio et al. 2013; Xu and Jaggers 2014). As Dillenbourg et al. (2013) have argued
‘massive scale can sometimes be best achieved by aggregating a massive number
of small learning cohorts, again highlighting the importance of small group dynam-
ics and the importance of scale-down’. Similarly, the founder of Khan Academy
(khanacademy.org), an online learning platform which provides access to videos and
mastery-based, sequential learning activities (arguably not an MOOC but certainly
fulfilling the criteria for massive, open and online), Sal Khan, argues that the power of
the model he has created is not in the online provision of content but rather in the shift
in offline pedagogy that the online content provides. That is, having access to high-
quality online content and structured learning activities allows teachers to develop
more innovative, active, personalised and community-oriented learning activities in
the physical classroom setting.
Despite the instructional design and pedagogical challenges associated with online
learning at a massive scale, the massive reach of MOOCs does represent a significant
opportunity in education. Social interaction is a critical component of learning, but
becomes problematic when massive numbers of learners outstrip the numbers tutors.
Learners are unlikely to receive tutor feedback; however, Ferguson and Sharples
(2014) suggest that, at their best, MOOCs offer learners access to support from a
wide range of other learners and facilitate the development of culturally diverse
perspectives. The importance of the social aspects of learning and the ability of
MOOCs to facilitate this have led to a social learning movement, which lobbies for
MOOCs to be designed around social interactions.
1.7 Open
Open education is not a new phenomenon. It first was associated with open uni-
versities worldwide and more recently with the broader open movement in edu-
cation, which among other dimensions incorporates Open Educational Resources
(OER) and Open CourseWare (OCW). These are resources freely available to
everyone with Internet access, which is an important proposition for many people
worldwide. Only 6.7% of the world’s 7.4 billion people hold a college or univer-
sity degree (Barro and Lee 2010). Therefore, OCW, OER, MOOCs and whatever
form they may evolve into are important, particularly in developing countries where
participation in higher education is low.
‘Open’ has multiple meanings in relation to MOOCs. It may refer to access;
anyone, no matter his or her background, prior experience or current context may
enrol in an MOOC (McAuley et al. 2010). Open can also refer to cost; that is, in theory,
MOOCs are available free of charge. Free education was a principle that underpinned
the development of the MOOC concept, though in practice many MOOCs are not
free of charge (Fischer et al. 2014). The third meaning of open relates to the open
nature of knowledge acquisition in an MOOC, including the employment of open
educational resources (OER) or Open CourseWare (OCW) which is available under
a Creative Commons licence that allows various levels of use (Caswell et al. 2008).
The fourth meaning is around knowledge production and the opportunity for the
remixing and reuse of resources developed during an MOOC by the instructors and
by the learners themselves to create new knowledge (Milligan et al. 2013).
It has been argued that with the rising cost of higher education, the increasing
demand for access to higher education and the growing need for people to engage
in learning throughout their lives in order to update their knowledge and skills, open
education provides a means for reducing economic, geographic and social barriers
to participation. In this context, Wilton and Hilton (2009) position openness as a
‘prerequisite to changes institutions of higher education need to make in order to
remain relevant to the society in which they exist’.
The original notions of openness in MOOCs, where education is free of charge
and courses are open to anyone, are being challenged. MOOCs are not always free
of charge. MOOC providers have been experimenting with a variety of business
models and pricing plans for MOOCs. These include paying for certification, to sit
a proctored exam, to receive course credit or to work towards a degree. Providers
have recognised the potential of appealing to the lucrative employment market and
the willingness of individuals to pay for learning opportunities that lead to greater
employability. For example, as mentioned earlier, while MOOC platform providers
continue to make most courses and materials available for free, learners may pay
for specific services such as certification or closed MOOC-based degree courses.
So MOOCs are not always open to anyone. Coursera has found that when money
changes hands, completion rate rises sixfold, from approximately 10 to 60% (Onah
et al. 2014). It further is not simply the cost that is potentially restricting access but
also the time it takes to engage in learning activities.
The current open access model, which allows anyone to enrol in an MOOC, is
also being challenged by research showing that not all learners have the necessary
autonomy, dispositions or skills to engage fully in an MOOC (Milligan et al. 2013).
While notions of the empowered individual and of learner-centred engagement pro-
vide alluring visions of what a utopian education system could be, the reality is more
complicated. As will be explored in more detail in Chaps. 2 and 3, many learn-
ers do not have the extant capability to navigate the informal, largely self-directed
nature of learning in MOOCs and the lack of support and interpersonal connections.
Increasingly questions are being asked about the balance between effectiveness and
openness in MOOCs, questions that will be returned to in chap. 4.
1.8 Online 11
1.8 Online
The online aspect of MOOCs is gradually being blurred, as MOOCs are being used
in conjunction with or to supplement in-person school and university classes (Bates
2014; Bruff et al. 2013; Caulfield et al. 2013; Firmin et al. 2014; Holotescu et al.
2014), expanding their scope to include blended learning contexts. In a review of the
evidence surrounding the integration of MOOCs into offline learning contexts, Israel
(2015) determined that while the blended approach leads to comparable achievement
outcomes to traditional classroom settings, their use tended to be associated with
lower levels of learner satisfaction. Downes (2013) suggests that for an online course
to qualify as an MOOC no required element of the course should have to take place
in a specific physical location.
While the online nature of learning in MOOCs is pivotal to their ability to open
up learning to ever greater numbers of learners, there are also payoffs, which are
often downplayed or disregarded. Selwyn (2016, p. 30) asks the following questions
of digital technology:
Just why should digital education be any more successful in overcoming educational inequal-
ity and disadvantage than previous interventions and reforms? Why should the latest digital
education be capable of overcoming entrenched patterns of disparity and disadvantage? What
is it that makes people believe that digital education will be different?
Selwyn goes on to suggest that there is a:

Notable dehumanization of the acts of learning and teaching that might be associated with
digital education …. current arrangements of digital education often have little to say with
regard to individuals’ relationships with others, and the social and political contexts in
which they learn and act. There is clearly a need to bring the human element of education
into technology. (Selwyn 2016, p. 146)
Too often MOOCs are positioned as an autonomous, decontextualised learning activ-

ity with little or no connection to the everyday lives and contexts of the learners.
However, as will be explored in Chap. 2, the learners’ offline context is pivotal to
their engagement in and ultimate experience of any online learning activity.
1.9 Course
Downes (2013) suggests three criteria that must be met for an MOOC to be cate-
gorised as a course: (1) it is bounded by a start and end date; (2) it is cohered by
a common theme or discourse; and (3) it is a progression of ordered events. While
MOOCs typically are bounded, this may manifest in different ways. MOOCs initially
started as structured courses, designed to parallel in-person, formal learning, such
as university classes, with start and end dates. However, an increasing number of
MOOCs are not constrained by specific start or end dates (Shah 2015), facilitating
a more flexible, self-paced model, which enables learners to complete a course at
their own pace. The length of courses also varies, with some constructed as a series
of shorter modules, which may be taken independently or added together to form a
longer learning experience.
The structure and degree of conformity in patterns of engagement vary substan-
tially among MOOCs. Conole (2013) suggests that participation can range from
completely informal, with learners having the autonomy and flexibility to determine
and chart their own learning journey, to engagement in a formal course, which oper-
ates in a similar manner to offline formal education. Reich (2013) has questioned
whether an MOOC is a textbook (a transmitter of static content) or a course because
of the conflicts that exist around confined timing and structured versus self-directed
learning, the tension between skills-based or content-based objectives, and whether
certification is included (or indeed achieved by learners).
Rather than focusing on issues of structured versus unstructured and informal
versus formal learning, Siemens (2012) argues that the real tension in how MOOCs
are conceived is between the transmission model and the construction model of
knowledge and learning. Siemens suggests that rather than being viewed as a course,
MOOCs should be conceptualised as a platform on which individual learners con-
struct and ultimately define their own learning.
These different conceptions of each of the terms, massive, open, online and course,
reflect the different ideologies and perspectives that drive the expansion of MOOCs.
The next section examines various ways these different perspectives have been con-
sidered.
1.10 MOOC Ideologies
Various MOOC ideologies can be seen in action, when looking at different MOOC
designs, learning activities and formats. Numerous typologies have been developed
in the literature, as an attempt to classify these different perspectives (Fig. 1.1). These
typologies represent an attempt to capture and classify the manner and presentation
of MOOCs.
MOOCs represent a multiplicity of perspectives and plurality of approaches,
which means that their value is not always transparent. Examples of these differ-
ent types of MOOCs are described below.
The early MOOC developers, particularly those who were not experienced in
designing for distance learning, designed MOOCs by replicating classroom-based
learning. These MOOCs were typified as ‘xMOOCs’, differentiating them from the
earlier ‘cMOOCs’, which were based on a ‘connectivist’ (networked) approach to
learning. xMOOCs are characterised by learners following a linear pathway through
course materials reminiscent of campus-based teaching. These materials include
video-based lectures, texts and online, test, based forums, designed to replicate class-
room discussions.
1.10 MOOC Ideologies 13
Fig. 1.1 Common typologies of MOOCs
The earliest Harvard edX MOOCs were designed as xMOOCs, Transfer MOOCs
(Clarke Typology) or Content MOOCs (Lane Typology). These courses intentionally
were designed to mimic the Harvard on-campus experience (Vale and Littlejohn
2014). For example, Quantitative Methods in Clinical and Public Health Research
(PH207X) was designed in 2012 around a campus-based course to teach learners the
basic principles of biostatistics and epidemiology, including outcomes measurement,
study design options and survey techniques. The Harvard faculty had little experience
of distance learning and decided to transfer sections of the face-to-face course onto
the edX platform by filming video lecture sequences interspersed with pictorial or
interactive illustrations and online articles.
In reality, the MOOC experience is very different from learning on the Harvard
campus. Crucially, the sociocultural experience of learning with other students and
with the Harvard Faculty is missing. In an attempt to reduce this deficit in their
learning some of the PH207X, students used social media tools, such as meetup.com,
to self-organise into face-to-face study groups. A meetup in Bangalore drew over 100
MOOC students.
Informal meetups in geographically distributed locations are sometimes designed
into an MOOC. For example, the Coursera MOOC, A Life of Happiness and Fulfill-
ment, offered by the Indian School of Business (www.coursera.org/learn/happiness)
had meetups designed and orchestrated by the instructor and supplemented by a Face-
book group organised by the students. These meetups were reminiscent of distance
learning ‘summer schools’, where students and faculty learning at a distance have
the opportunity to interact. In most cases, MOOC faculty are unable to join these
meetings, because of the large number and geographic dispersion of these gatherings.
The view of an MOOC as being equivalent to a campus-based course is problem-
atic for learners in countries, such as India or Malaysia, where governments view
MOOCs as a way to scale up the higher education system. These governments need
to open up education on a massive scale. While MOOCs can open up access to high-
quality education for people who have limited options, there should be recognition
that learning in an MOOC is qualitatively different from learning on a campus.
These differences in where and how learners and tutors interact illustrate a dis-
tinction between online and face-to-face learning. Online learning does not replicate
learning while physically present (Selwyn 2014). It offers a distinct experience with
potential advantages of distance, time and forms of interaction, but does not provide
the same sociocultural experience as learning face-to-face with others. People are
not embedded within a learning community in the same way.
Some MOOCs have been designed around communities of people with a shared
interest, rather than based on predefined objectives. For example, #PHONAR (phonar.
org) is an open, online photography course where learners interact with experts who
help them develop online portfolios of photographic images. Learners have to be
proactive, taking responsibility for building and nurturing connections with peers and
experts and to source resources to support their learning. The decentralised nature
of the Internet provides an ideal environment to support the development of an open
and participatory culture of knowledge building through collaboration, participation
and engagement. In PHONAR, each student sets out personalised learning goals, and
the course topics tend to be emergent and responsive to the immediate needs of the
learners, rather than pre-prescribed. This approach is different from most MOOCs,
1.10 MOOC Ideologies 15
where the curriculum and objectives and course content tend to be predefined by the
course provider.
Other examples of online courses based around learning communities include
crowdsourcing platforms or virtual laboratories where people gather and upload
data to a shared platform (Wiggins and Crowston 2011). An example is iSpot
(ispot.org.uk), where nature lovers are encouraged to engage in participatory learn-
ing by gathering and sharing data on flora and fauna. Active learning opportunities
are generated as enthusiasts upload data and experts offer feedback. iSpot is part of
OPAL—Open Air Laboratories—an initiative of Imperial College London and The
Open University in the UK which aims to encourage people to explore, study, enjoy
and protect their local environment. iSpot is not a course in the conventional sense, but
it is massive, open and online. Other citizen science, crowdsourcing environments
include Galaxy Zoo (www.galaxyzoo.com), where enthusiasts assist professional
scientists in the morphological classification of large numbers of galaxies.
MOOCs have been designed around the free flow of data and knowledge.
For example, Introduction to Datascience, an MOOC run by the University of
Washington and Coursera, focused on learners learning data science by creating
and sharing codes. This type of course design is particularly useful for professional
development because professionals can learn through engaging in real work tasks,
for example, creating code needed for a work task.
Another MOOC that supported the development and exchange of profes-
sional knowledge was the Evidence-Based Midwifery Practice MOOC (www.
moocformidwives.com) which was led by Midwifery academics in Australia and
Denmark in April and May 2015. Midwives located in different countries were
encouraged to exchange ideas about how their practice fitted within their diverse
geographic and cultural contexts. Professional learning is a growth area for MOOC
development, possibly because professionals are likely to have developed ability to
engage actively in learning, requiring less support than less experienced learners.
As MOOC designs evolved, some courses were based around and run syn-
chronously with political events. Examples include The Scottish Independence
MOOC, run by the University of Edinburgh and FutureLearn in 2014 and the Euro-
pean Culture and Politics MOOC, run by the University of Groningen and Future-
Learn in 2016. These MOOCs encouraged participants to consider the implications
of Scottish Independence and the impact of Britain leaving the European Union,
respectively.
Future MOOCs are likely to make more use of data analytics, virtual reality,
simulation and gaming environments. For example, 3D virtual reality (VR) or gaming
environments afford students opportunity to collaborate in simulations. Virtual reality
is helpful in subjects where visualisation is important, such as molecular modelling
in chemistry or building design in architecture. VR supports learning in professional
contexts where experimentation in simulated real-life scenarios supports learning,
such as nursing or business.
Although VR and gaming are used in these subject areas, there are few examples
of MOOCs that are based on VR, gaming and simulations. This possibly is because
of the expertise required as well as time limitations for MOOC developers. How-
ever, these technologies are on the horizon for integration into MOOCs. Platform
providers are experimenting with integrating gaming environments with the MOOC
platforms to allow MOOC learners to experience simulations. One example is the
EADVENTURE platform, developed by the Universidad Complutense de Madrid to
allow non-technical users, including tutors and learners, to create and modify games
that can be integrated into the edX platform (Freire et al. 2014).
This section has illustrated the multiple belief systems that underpin MOOCs.
These ideologies lead to different approaches that do not always produce the intended
outcomes. This book aims to interrogate these belief systems and investigate some
of the unplanned, or unseen, consequences.
1.11 The Ambitions of This Book
In this book, we attempt to set out a broad and balanced view of massive open online
courses, with a particular focus on questioning the extent to which MOOCs are a
disruptive and democratising force in education. This results in an extended focus on
the nature and processes of learning in MOOCs and the roles, actions and ontogenies
of learners—both as a collective and as individuals.
Chapter 2 introduces the tension in MOOCs between their ability to exponentially
increase the number of learners accessing educational opportunities and their ability
to provide equal opportunities and outcomes to all those learners. We argue that the
majority of MOOCs are designed to be used by people who are already able to learn,
thereby excluding learners who are less prepared to learn independently and without
direct tutor support. The corollary of this argument is that without taking action to
ensure everyone has the ability to engage with and benefit from this expansion of
learning opportunities, we will not democratise learning
Chapters 3 and 4 build on Chap. 2 to explore how the emphasis on the individual as
active and autonomous learner sometimes conflicts with the expectation that learners
conform to accepted norms. This expectation that learners conform to accepted ‘ways
of being’ in an MOOC isolates those who plan their own pathway. We develop a new
typology of learner types, which an individual may move between depending on their
motivations. We argue that given the centrality of the learner to charting their own
engagement and determining their own outcomes, MOOCs must move beyond their
current focus on traditional educational approaches and outcomes. This requires the
utilisation of sophistical algorithms and analytics that incorporate a human element
to ensure learning is not simply scaffolded by course materials and rudimentary
analytics, but that there is always a tutor, expert or peer the student can learn with.
Chapter 5 explores notions of quality in MOOCs. It questions whether the current,
predominantly traditional metrics and measures are suited to the nature of learning
in MOOCs. We argue that the increased reliance on data analytics is skewing how
we view quality in MOOCs and that data around learner engagement and interaction
has to be interpreted in new ways that are consistent with the new ways of learning
in MOOCs, rather than being based on conventional online learning.
1.11 The Ambitions of This Book 17
Chapter 6 examines the broader societal dimensions fueling the expansion of

MOOCs, exploring a tension between the perspective of an MOOC as a set of products
(content and credentials) on sale to students with the notion of an MOOC as a means
of exchanging knowledge and transforming the learner.
This chapter illustrated that the term ‘MOOC’ is being used to describe almost
any form of online learning. Consequently, many of the ideas raised throughout this
book will be applicable not only to MOOCs but also to online learning in general.
The MOOC, therefore, operates as a form of educational case study and a backdrop
or context against which to position the research and ideas that are pivotal to under-
standing the changing educational landscape. We hope this critique can stimulate the
thinking and debate around MOOCs and online learning.
Acknowledgements The authors wish to thank Vasudha Chaudhari of The Open University for
comments and for proofing this chapter.
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Chapter 2
The [Un]Democratisation of Education
and Learning
Abstract MOOCs have engendered excitement around their potential to democra-

tise education. They appear to act as a leveller and offer equal opportunity to millions
of learners worldwide. Yet, this alluring promise is not wholly achieved by MOOCs.
The courses are designed to be used by people who are already able to learn, thereby
excluding learners who are unable to learn without direct tutor support. The solu-
tions to this problem tend to focus on the course, as ‘learning design’ or ‘learning
analytics’. We argue that effort needs to be focused on the learner directly, support-
ing him or her to become an autonomous learner. Supporting millions of people to
become autonomous learners is complex and costly. This is a problem where edu-
cation is shaped principally by economic and neoliberal forces, rather than social
factors. However, ‘automated’ solutions may result in attempts to quantify learn-
ers’ behaviours to fit an ‘ideal’. There is a danger that overly simplified solutions
aggravate and intensify inequalities of participation.
2.1 The Hype, De-hype and Re-hype of MOOCs
In the past, MOOCs were positioned by governments, universities and other organi-
sations as potential disruptors to the educational status quo. At their most innovative,
MOOCs are challenging traditional educational and learning paradigms, where learn-
ing typically is teacher-directed and structured within a formal institute. They break
down divisions between those who can access prestigious educational institutions
and those who cannot, opening up high-quality content to anyone who has an inter-
net connection and device, and providing continued learning opportunities to ever
greater numbers. New technological infrastructure and digital technologies are not

https://doi.org/10.1007/978-981-10-8893-3_2
22 2 The [Un]Democratisation of Education and Learning
only providing access but also enabling new approaches to learning and the reposi-
tioning—if not in practice then theoretically—of learners, educators and institutions.
As Selwyn observes:
the ever-expanding connectivity of digital technology is recasting social arrangements and
relations in a more open, democratic, and ultimately empowering manner. (Selwyn 2012, p. 2)
The early promise of MOOCs democratising education and providing future-focused,

relevant, high-quality learning for all through improved access and radically new
forms of learning may have subsided in recent years, particularly in the Western
world. However, the potential of MOOCs and online learning to revolutionise edu-
cation still dominates the rhetoric, particularly in the developing world where gov-
ernments are trying to expand higher education rapidly. India alone has expanded
its system to accommodate 8 million more students through opening up 20,000
universities and colleges over the period 2001–2011. Corporations and technology
companies also recognise the potential of MOOCs to scale up professional training.
Private and public organisations seeking to provide much needed continual profes-
sional development to upskill their workforce have generated renewed enthusiasm
for MOOCs. This excitement was captured The Economist in January 2017, herald-
ing ‘The Return of the MOOC’ and championing the role that alternative providers
must play in solving the problems of cost and credentialing in education (‘Equipping
people to stay’ 2017).
However, the current reality of learning in MOOCs remains somewhat distant
from this alluring promise. There continues to be considerable variation both in the
nature of learning that MOOCs offer and the ways in which individuals choose to
engage with them. As the authors have previously noted:
The specific nature and composition of individual MOOCs are profoundly shaped and ulti-
mately the product of their designers and instructors, the platform and platform provider, and
the participants, all of whom bring their own frames of reference and contextual frameworks.
(Hood and Littlejohn 2016, p. 5)
While MOOCs may be pushing boundaries and challenging existing models and
paradigms, they also, in many ways, are reinforcing traditional patterns and
behaviours in both learning and learners, as well as in institutional structures, ideas
that will be returned to throughout the book.
The focus of this chapter is to provide a research-informed exploration of the
potential, promise and pitfalls of MOOCs and investigates online and open learning
more generally.
2.2 The Learnification of Education; the Wider Context of MOOCs 23
2.2 The Learnification of Education; the Wider Context

of MOOCs
We shouldn’t underestimate the ways in which language structures possible ways of thinking,
doing and reasoning to the detriment of other ways of thinking, doing and reasoning. (Biesta
2009)
Language plays an important role in shaping how we understand and position our-
selves in relation to different opportunities and ideas. Nowhere is this more apparent
than in the language being used to discuss education and learning. To understand
MOOCs, it is necessary to understand, or at least be aware of, the broader educa-
tional contexts in which they are being developed. MOOCs not only are responding
to technological advances, particularly the social web which has made possible their
massive scale and global reach, but also changing political contexts and economic
imperatives that call for the expansion of higher education on an exponential scale
by using qualitatively new approaches to learning (Liyanagunawardena et al. 2013;
Kennedy 2014).
Higher education has been linked to national economic growth, with the most
developed economies having the highest proportion of graduates in their population
(Hanushek et al. 2008). This link means that populous countries, such as China and
India, need to rapidly expand their university sector to ensure capital growth (ICEF
Monitor 2012). In parallel, the emergence of work practices that are continuously
changing and the need to solve bespoke, ill-structured problems under various levels
of uncertainty, results in a growing demand for new and adaptive forms of person-
alised learning that focus on learners and their specific learning needs (Daniel et al.
2015). Many MOOCs, designed as a collection of texts and videos, do not promote
this sort of adaptive and personalised learning (Margaryan et al. 2015). Neverthe-
less, it is understood that these types of courses can have a formative role in higher
education, particularly to expand higher education in less economically developed
countries, professional learning and training and lifelong learning (Daniel et al. 2015).
The dominant paradigms and approaches surrounding the world of MOOCs are
rooted in the contemporary political discourse around education. It is what Biesta
(2009) has referred to as the ‘learnification of education’, or the ‘new language of
learning’. This new language is framed by the notion that, with the advent of the
knowledge society and the exponential development of digital technologies, a new
educational paradigm is required; society requires a shift in mindset to focus on
notions of lifelong learning, and learner-centric educational models.
As the Commission of European Communities advocated in 1998:
Placing learners and learning at the centre of education and training methods and pro-
cesses is by no means a new idea, but in practice, the established framing of pedagogic
practices in most formal contexts has privileged teaching rather than learning. (…) In a
high-technology knowledge society, this kind of teaching-learning loses efficacy: learners
must become proactive and more autonomous, prepared to renew their knowledge contin-
uously and to respond constructively to changing constellations of problems and contexts.
The teacher’s role becomes one of accompaniment, facilitation, mentoring, support and guid-
ance in the service of learners’ own efforts to access, use and ultimately create knowledge.
(Commission of the European Communities 1998, p. 9, quoted in Field 2000, p. 136)
Biesta (2009) argues that a new language of learning currently dominates education:
The ‘new language of learning’ is manifest, for example, in the redefinition of teaching as
the facilitation of learning and of education as the provision of learning opportunities or
learning experiences; it can be seen in the use of the word ‘learner’ instead of ‘student’ or
‘pupil’; it is manifest in the transformation of adult education into adult learning, and in the
replacement of ‘permanent education’ by ‘lifelong learning’. (pp. 37–38)
This shift towards the learnification of education extends across the domains of
research, policy and practice (Illeris 2009, 2014). The language of learning denotes
a new positioning of the role that learning (as opposed to education) plays within
society and the economy, and the presumed or desired changing roles and power
relations of key players in the traditional education.
MOOCs are at the heart for this changing power structure, promoting a recon-
ceptualisation of the intersection and interplay among the learner, the instructor, the
institutional provider and the outcomes of the combined activity and learning pro-
visions. MOOCs, in many ways, are the ultimate encapsulation of this shift towards
learning. The earliest discussions of MOOCs focused on the new roles and responsi-
bilities of learners in a networked learning environment where all participants were
responsible for contributing to the discourse and knowledge that was shared (Downes
2012). These courses were highly experimental and were considered groundbreaking
in the way they enabled learners, rather than teachers and experts, to determine how
learning should take place. Their design was based on a network approach to learn-
ing, sometimes described as a ‘connectivist’ (or cMOOC) approach—see Chap. 1
for a typology of MOOCs.
There is currently little evidence to support connectivism as a theory, but it can be
considered as an approach to learning conceptualised as participation in a network
(Siemens 2014; Downes 2012). It views people as nodes in a digital network, with
the connections between nodes as learning. The learner assembles and constructs
knowledge within the network, for example, by creating blogposts, microblogposts
or other forms of media. These media are shared with other learners and with experts,
who can edit or comment. In this way, the connectivist approach has parallels with
theories of constructivism, where learners construct knowledge and are guided by a
more expert ‘teacher’.
A new wave of MOOCs that emerged in 2011 and 2012 were designed around
a different, instructivist approach. In instructivist pedagogical practices, the teacher
sources and assembles knowledge in the form of artefacts for the student to use. These
instructivist MOOCs have been termed xMOOCS. They aimed to allow anyone,
anywhere to have access to the same (or similar) sorts of formal education that
students experience on campus in universities. Therefore, they were designed around
online versions of lectures, readings and discussions that characterise traditional
university learning. In reality, the use of these artefacts online is qualitatively different
from an on-campus experience. Also many universities have evolved their teaching
from courses where students work through a set of materials predefined by the teacher
to approaches to learning where the learner constructs knowledge, for example,

or ‘peer-based learning’ where students learn from one another through creating a
product or ‘studio based teaching’ where students build portfolios of work.
This instructivist approach contrasts with the connectivist perspective described
earlier. It could be argued that the connectivist approach is more democratic than
traditional approaches to online learning, typified by xMOOCS, since it emphasises
the importance of the learner, rather than the teacher, assembling and sharing knowl-
edge. However, as we will explore further in this book, cMOOCs may not allow for
democratic participation, since the course design presupposes learners are willing to
engage in their own learning in specific ways.
Another problem with the cMOOC approach is that some learners do not have
the cognitive, behavioural or affective characteristics necessary to actively determine
their own learning pathways. Research has provided evidence that learners do not
always have the inclination, digital capability or the degree of confidence and self-
efficacy required to actively participate (Littlejohn et al. 2016). Thus, the emphasis
on the individual as active agent in their learning journey is privileging those who
can learn.
Furthermore, the idea of creating knowledge publically and behaving visibly as
an expert may lead towards a western cultural approach (Knox 2016), yet MOOC
stakeholders claim MOOCs take a ‘global’ perspective (Godwin-Jones 2014). Thus,
the assurance that everyone has the ability to democratically engage in learning in a
MOOC is not evident.
In summary, the rhetoric around both cMOOCs and xMOOCs is centred on their
ability to democratise learning by enabling anyone, anywhere to access learning
opportunities. Yet, MOOC providers and designers repeatedly have downplayed or
ignored the critical need for active agency and self-regulation from the learners, and
have assumed all learners were equipped to learn independently. Attempts to resolve
this problem have focused around designing solutions into the MOOC, rather than
focusing on enabling the learner (Guàrdia et al. 2013).
The MOOC represented a new approach, if not to replace, at least to supplement
and compliment the old establishments of education. Their conception and promotion
is bound in the understanding of the need for new opportunities and new approaches
to learning and accreditation that breaks free from the rigid constraints of traditional
educational institutions, especially universities. They were seen as taking power
away from universities and placing it into the hands of individuals who were able to
actively shape their own learning journeys.
Biesta (2009) suggests that there are four trends playing into this new language of
learning: (1) new theories of learning and more particularly [neo]constructivist theo-
ries that position active student engagement at the heart of learning; (2) postmodern
critiques of the notion that education can and should be controlled by teachers; (3)
themes of lifelong learning and the need for everyone to continue to learn throughout
their life; and (4) the rise of neoliberalism and the prioritisation of the individual,
which positions the student as consumer and shifts education from being a right to
being a duty. MOOCs appear to respond to all four of the trends Biesta identifies,
although perhaps most strongly with the latter two.
MOOCs were built on the emancipatory properties of the new language of learn-
ing, suggesting that individuals have the potential and ability to take hold of their
own learning. Biesta (2005) suggests that ‘Teaching has, for example, become rede-
fined as supporting or facilitating learning, just as education is now often described
as the provision of learning opportunities or learning experiences’ (p. 55). In this
conception, one favoured by policymakers around the world, the learner has agency
to determine and shape their own engagement, with the teacher acting as facilitator
and the provider a mediator of the learning experience.
This new conception of what it means to learn, how learning should be structured,
and the position and role of the individual learner within this is championed by
a growing number of organisations. Dua (2013), a Senior Partner at McKinsey &
Company, claims:
What most people—including university leaders—don’t yet realize is that this new way
of teaching and learning, together with employers’ growing frustration with the skills of
graduates, is poised to usher in a new credentialing system that may compete with college
degrees within a decade. This emerging delivery regime is more than just a distribution
mechanism; done right, it promises students faster, more consistent engagement with high-
quality content, as well as measurable results. This innovation therefore has the potential to
create enormous opportunities for students, employers, and star teachers even as it upends
the cost structure and practices of traditional campuses. Capturing the promise of this new
world without losing the best of the old will require fresh ways to square radically expanded
access to world-class instruction with incentives to create intellectual property and scholarly
communities, plus university leaders savvy enough to shape these evolving business models
while they still can. (p. 1)
The Economist (“Equipping people to stay” 2017) similarly presents the new possi-
bilities offered by MOOCs and how these are challenging traditional paradigms and
institutions:
The market is innovating to enable workers to learn and earn in new ways. Providers from
General Assembly to Pluralsight are building businesses on the promise of boosting and
rebooting careers. Massive open online courses (MOOCs) have veered away from lectures on
Plato or black holes in favour of courses that make their students more employable. At Udacity
and Coursera self-improvers pay for cheap, short programmes that bestow “microcredentials”
and “nanodegrees” in, say, self-driving cars or the Android operating system. By offering
degrees online, universities are making it easier for professionals to burnish their skills. A
single master’s programme from Georgia Tech could expand the annual output of computer-
science master’s degrees in America by close to 10%. (p. 3)
This quote illustrates Biesta’s (2009) fourth trend about the neoliberal influence on
direction, governance and design of education, as well as how MOOCs are becoming
an agent of this societal shift.
Learning, learners and learning outcomes are being reshaped following eco-
nomic imperatives. The focus is, therefore, on financial benefit, rather than on social
growth. They play on notions of the mismanagement of education, as Selwyn (2016)
describes:
…. sense of the mismanagement of education by monolithic institutions that are profoundly

undemocratic and archaic. These are lumbering organisations where ownership, control
and power are concentrated unfairly in the hands of elites – be they vice chancellors and
university professors, or school district superintendents, tenured teachers and their unions.
Like many large administrations and bureaucracies, these institutions that are believed to be
unresponsive, incompetent, untrustworthy, ungrateful, self-serving and greedy. (p. 11)
For the past decades, education increasingly has been dictated principally by eco-
nomic rather than social or learning imperatives and outcomes (Olssen and Peters
2005), themes that will be explored in the coming chapters at length.
The economic pressures within education are linked to the expansion of higher
education. Put simply, educating more students requires more funding. Arguably,
the countries that are finding this most difficult are those where university education
has been subsidised significantly by the government but this funding has recently
been reduced. For example, the United Kingdom, New Zealand, Australia and also
Finland, where Finland which recently introduced fees for non-EU students. Funding
regimes have changed, and fees have been introduced or dramatically increased,
requiring societal changes if the population does not have a culture of paying for or
taking out loans to finance educational opportunities. MOOCs are believed to be a
solution; however, without clear business models, the economy driving MOOCs has
been ambiguous and unsound.
Both Dua (2013) at McKinsey and The Economist (‘Equipping people to stay’
2017) warn that while a combination of new models and technological infrastructure
is facilitating a dramatic shift in the ways in which learning is financed, offered and
engaged with, the current reality remains somewhat distant from the promise. How-
ever, these now common conceptions of emancipatory learning opportunities and
the learner-centred, learner-directed nature of learning opportunities are influencing
how learning is structured and how individual learners or students are described and
positioned within the MOOC.
However, while the emancipatory aspects of a MOOC are possible, Biesta (2009)
warns that:
The absence of explicit attention for the aims and ends of education is the effect of often
implicit reliance on a particular ‘common sense’ view of what education is for. We have to
bear in mind, however that what appears as ‘common sense’ often serves the interests of
some groups (much) better than those of others. (p. 36)
In the case of MOOCs, there is a seductive notion of the idea that they are for
everyone, making learning and education readily accessible. But the reality is more
nuanced than this.
The extent to which MOOCs have actually achieved their democratising mission
remains somewhat contentious. MOOCs hold an uncertain space, appearing simul-
taneously to challenge traditional approaches and paradigms, while continuing to
draw on and replicate existing educational and learning models.
The largest providers of MOOCs are still the elite universities and large multi-
national corporations. And while the language of MOOCs represents the shift to
learnification, MOOCs still largely are utilising traditional educational metrics to
measure success. What it means to learn has not shifted dramatically from tradi-
tional notions or conceptions. Completion and certification of learners still remain
the most frequently used metrics for denoting success and quality in a MOOC. A
focus undermines the inherent flexibility in the MOOC, which enables individuals
to determine and chart their own journey in a MOOC and to self-determine what it
means to be successful.
The hint of diversity and self-constructed learning is subsumed within preordained
goals and an overarching agenda established by the MOOC creator. This perhaps is
particularly apparent in the shift away from openness in MOOCs towards a user-pays
model. MOOCs are being subjected to the same pressures and forms of operating that
shape traditional institutions. They become a new form of education, with creden-
tialing—an essential element of educational systems—becoming the driving factor.
2.3 Towards Democracy
This chapter has outlined that the democratisation of education can be conceived in
several ways:
First, it can be imagined as the expansion of education, facilitating equal access
to learning opportunities for everyone. However, as this chapter argued, this form of
democracy requires not simply an expansion in the numbers of learners, but also the
assurance that everyone has the ability to actively engage in learning. Equality of
access does not necessarily equate to equality of participation. Alternatively, demo-
cratic learning could be viewed as a shift from teachers and experts deciding what
is to be learned and how learning should take place, to learning goals, outcomes and
behaviours being at the will of the learners themselves. This section examines each
of these perspectives in turn.
At first glance, the expansion of university courses as MOOCs appears to allow
everyone (or at least those with access to the web) equal access to learning oppor-
tunities. Chapter 1 illustrated that sometimes MOOCs try to replicate conventional
higher education in elite institutions (in other words, access to renowned faculties).
However, MOOCs cannot offer the grandeur of the physical space of the privileged
and influential universities (Knox 2016). The distinction between face-to-face and
‘distance’ education can serve to downgrade the status of MOOCs, having the impact
of making sure MOOC learners are kept ‘in their place’, and privileging those who
are able to be ‘present’, rather than emphasising equality (ibid). This phenomenon is
particularly significant where the university offering a MOOC has unrivaled campus
facilities.
Some MOOC platform providers measure learning by identifying whether the
learner follows course pathways as directed by the tutors, and whether he or she
completes the course. These assumptions about what behaviours indicate whether
a student is learning provide little scope for the individual to decide the forms of
2.3 Towards Democracy 29
engagement that are best suited to his or her motivations and needs. Rather than
freeing the learner, these measures appear to tie the learner to a specific, predefined
learning pathway.
Research tells us that there are many ways learners participate in MOOCs and
that they do not always follow course pathways (Milligan et al. 2013). These dif-
ferent forms of participation, detailed in Chap. 4, are manifest in different forms
of engagement, ranging from ‘active’ engagement to ‘invisible’ involvement, where
the course facilitators are not aware of whether or how a participant is learning. The
conventional view of education privileges the active approach, and there is empirical
evidence that active participants are frustrated by those who these learners do not per-
ceive as active (ibid). Yet, some participants who appear unseen and invisible to other
learners and course facilitators report positive experiences of learning. However, this
type of behaviour does not fit well with dialogic pedagogies that emphasise people
coming together to share their own unique viewpoints, questioning whether learners
have a duty to participate actively in education, not only for their own learning but
for the learning of others.
To ensure MOOCs support a more democratic form of learning, there needs to be
a reconceptualisation of the ways learning goals, outcomes and expected behaviours
in MOOCs can be determined by the learners, rather than by teachers.
Yet, it seems the possibility of this reconceptualization is receding. Learning ana-
lytics are being embedded into MOOC platforms to measure ‘engagement’ as defined
by the course facilitators, rather than by the learners themselves. Analytics data are
visualised in dashboards that measure learner behaviour, completion and achieve-
ment in assessments. If a learner chooses to behave or engage in the MOOC in ways
that are not predefined and standardised, the data gathered and analysed may give
negative signals about the learner. For example, a learner who is actively engaged
outside the MOOC platform, or who drops in and out of a MOOC to engage with only
what he or she wants to learn, may not appear ‘engaged’ or ‘active’ in an analytics
dashboard. Some analytics are based on the assumption that there is a correlation
between engagement and behavioural activities, such as browsing and exploring,
or completion, for example, MOOCs for Development (MOOCs4Dev) analyse all
types of engagement within the course platform to assess learner achievement (see
https://issuu.com/delta51/docs/mooc_report_final_30_11). However, these assump-
tions presuppose that the learner wishes to follow a learning pathway predefined by
the course designer. If MOOC learning is to be viewed as democratic, these measures
and assumptions have to be reconceptualised.
Correlations associated with learner ‘completion’ are a measure of carrying out
the activities determined by the course design, rather than an indicator of what the
learner might have learned. This measure assumes that learners want to complete a
course or even pass an assessment. However, these assumptions may not be valid
in a MOOC. Learners may have set their own learning goals and learned what they
wanted to learn, rather than following the course pathway and goals. There are calls
to link learning analytics with learning design to ensure that MOOCs are designed to
optimise learner progression and completion (see for example Lockyer et al. 2013).
However, the idea of adhering to an optimal, standardised design may not allow for
democratic behaviours where the learner, rather than the tutor, decides what is to be
learned and how. Though what these systems can offer are recommendations for the
learner to consider and act upon. For example, recommender systems can suggest
readings, further courses or people to link with in a ‘just in time’ way depending on
what the learner is currently learning and how they are learning.
2.4 Different Challenges, Same Outcome
The use of ICT in teaching and learning is becoming a key component in educational
policies of developing countries. Arguably, MOOCs can make an impact in terms of
opening access to higher education in developing countries, where access rates are
low.
The tensions experienced by MOOC learners in the developed countries, for exam-
ple, the need to be able to learn pro-actively, also affect learners in developing coun-
tries. However, some of the challenges associated with ensuring access to education
in developing countries are different from those in the developed world and such
as poor infrastructure, limited digital capability, social and cultural inequalities and
learning and teaching quality issues. Even where people have access to higher educa-
tion, the quality of learning and teaching may be poor. For example, the government
in India has flagged poor quality teaching in some universities, particularly smaller,
private institutions, as a key problem in higher education in the country.
Around 4 billion people around the world do not have Internet access. These
people are mainly in developing countries, where good digital infrastructure may be
restricted to major urban areas and rural areas may have unreliable or no electricity,
let alone Internet. In countries like Nigeria or Sri Lanka where students may commute
to access Internet Cafes, claims about enhanced learning through MOOCs may not
hold true (Anderson 2013).
Even where Internet is available, it may be slow, restricting the ability to stream
MOOC content (Liyanagunawardena et al. 2013, p. 4). Access to good digital tech-
nology tools can be limited and cost makes these tools less available. Reduced avail-
ability to digital tools can limit digital capability within the population, which makes
learning in a MOOC difficult. There are also issues associated with cultural diversity.
Some developing countries have diverse ethnic communities speaking different lan-
guages. India has twenty-two official languages, Zimbabwe sixteen, which makes it
challenging to provide equal opportunity to all groups unless they share a common
language. People in ethnic minorities can experience discrimination and unequal
access to educational opportunities.
Another problem is that some MOOC platforms, such as the for-profit Coursera,
operate under strict copyright rules, limiting their use in developing countries. Thus,
open-source platforms, such as that used by edX, have the advantage of giving local
educators control over the applications, content and curriculum. To address this issue,
2.4 Different Challenges, Same Outcome 31
some MOOC providers, for example, MOOC providers in India supported by the
Commonwealth for Learning, are building their own platforms in order to influence
developments.
Despite these challenges, MOOCs are viewed in the developing world as a use-
ful mechanism to scale up higher education. There is a recognition that develop-
ing nations may lose be vulnerable to neo-colonial effects associated with studying
MOOCs largely based on Western knowledge and cultural and philosophical assump-
tions. This issue has led some governments to develop policy and platforms to expand
higher education using ICT and MOOCs.
In India, for example, the government aims rapidly to expanding the higher edu-
cation system. India is one of the fastest growing economies, yet, only 18% of the
population participated in higher education in 2014, compared with 26% in China
and 36% in Brazil and over 50% in many developed countries (British Council 2014).
By 2020, the Indian government wants to increase the number of higher education
places by 14 million to reach a target of 30% participation. To help achieve this goal,
the government has invested in the development of a MOOC platform, SWAYAM
(Study Webs of Active Learning for Young Aspiring Minds), and courses. By intro-
ducing India-focused policy, platform and courses, they aim to address challenges
specific to the country.
Some countries have taken a different approach by partnering with international
organisations to provide access to higher education in areas where skill shortages
have been identified. The World Bank funded an initiative with the Coursera platform
to provide MOOCs for students in Tanzania to enable them to develop IT skills
relevant for private sector employment tracks (Boga and McGreal 2014). For students
in rural areas, the ability to access these MOOCs via mobile phones is crucial. A
number of private organisations have sponsored MOOCs as a way to identify future
talent for their workforce. Although there are a number of ethical issues associated
with this approach, it can be viewed by people, particularly in developing countries
where opportunities are limited, to offer huge opportunities. However, while MOOCs
go some way in supporting developing countries in facing different educational
challenges compared with developed countries, MOOCs still benefit most those who
are able to self-regulate their learning, leaving the most disadvantaged behind.
2.5 New Name, Repeating Model
Exploring the myriad of ways MOOCs are being conceptualised and offered prompts
the questioning of claims that MOOCs, as a rule, democratise learning. While they
are positioned as outside traditional educational provisions and structures, resulting
in the ability to shift conventional ways of conceptualising education and learning
leading to a redistribution of power, the reality is somewhat different. MOOCs, on
the whole, are very much embedded within the existing power structures and the
control of the pre-eminent institutions.
MOOCs appear to be a response by the education sector and advocates of open

learning to try to retain key aspects of conventional forms of education. The ways
in which MOOCs are designed and evaluated tend towards standardised design and
normative forms of participation, rather than focusing on personalization and meeting
the needs of the learners. In this way, MOOCs are not as open to student needs and may
not be as democratic as claimed ideas that will be explored further in the following
chapters.
MOOCs in many ways have focused on the potential and affordances of technol-
ogy to revolutionise education, or at least to shift the balance of power away from
traditional institutions and towards individual learners. However, in doing so, they
often pay too little attention to the ways in which technology is utilised, both by
designers and by the learners. As Kellner (2004) warns:
Technology itself does not necessarily improve teaching and learning, and will certainly not
of itself overcome acute socioeconomic divisions. Indeed, without proper re-visioning for
education and without adequate resources, pedagogy and educational practices, technology
could be an obstacle or burden to genuine learning and will probably increase rather than
overcome existing divisions of power, cultural capital, and wealth. (p. 12)
There is a great deal of rethinking needed before we can consider MOOCs as a form
of democratisation of education.
2.6 Concluding Thoughts
The theme of the [un]democratisation of MOOCs is returned to throughout this

book. This chapter has explored the tensions that exist between the potential of
MOOCs to offer a new reality and order in education, which is more just, fair and
open, aligning with the neoliberal agenda of the learnification of education and
individualism. However, as we have started to explore, the realities are more complex,
in particular the ability for everyone to participate in, and reap the rewards of this
new open education. Chapters 3 and 4 will build on these ideas to explore diversity
among learners and the variation in learning in MOOCs.
Acknowledgements The authors wish to thank Vicky Murphy of The Open University for com-
ments and for proofing this chapter.
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Chapter 3
The Emancipated Learner? The Tensions
Facing Learners in Massive, Open
Learning
Abstract MOOCs have the potential to challenge existing educational models. Para-
doxically, they frequently reinforce educational conventions by requiring the learners
to conform to expected norms of current educational models. Recent research has
produced data on how learners engage in MOOCs. And yet, despite the extensive
data, rather than freeing learners to chart their own pathways, MOOCs still require
the learners to conform to expected norms. The very act of learning autonomously
often causes tensions, most noticeably when learners choose to drop out, rather than
complete a course as expected, or when they engage in MOOCs as mere observers,
rather than active contributors. In this chapter, we explore how the emphasis on the
individual as active and autonomous learner sometimes conflicts with the expecta-
tion that learners conform to accepted norms. This expectation that learners conform
to accepted ‘ways of being’ in a MOOC isolates those who plan their own path-
way. The chapter concludes with a typology of different learners, arguing that, rather
than adhering to a ‘type’, each MOOC participant moves across these learner types,
depending on their motivations, and may span different types, rather than falling into
one single category.
3.1 Individual Learner, Common Challenges
MOOCs have the potential to provide as many different learning experiences as

there are learners. Each learner engages differently, guided and influenced by their
own motivations and goals. Chapter two explored this potential of MOOCs as a
move from conventional ‘education’ to broader forms of ‘learnification’. In this
chapter, these ideas are extended to explore how changes in language are shaping
our understanding and conceptualisation of what it means to engage in a MOOC
(or learning more generally) and how this influences the process and product of the

https://doi.org/10.1007/978-981-10-8893-3_3
36 3 The Emancipated Learner? The Tensions Facing Learners …
MOOC experience. Embedded within this new learning are assumptions about what
it means to be a learner, and in particular the myth of the universal learner.1
Biesta (2009) suggests that the move towards the learnification of education acts
to emphasise the centrality of the individual learner, not only in the learning process
but also within the structures that shape and mediate learning experiences. This
apparent focus on the learner and learner-centred or learner-oriented design is devised
to suggest an empowerment of the learner and their emancipation from traditional
institutions that controlled education. This chapter will explore how these ideas are
shaping the concepts of learners and learning in MOOCs, in particular picking up on
Biesta’s warning of the dangers in subscribing to this idea.
Rensfeldt (2012) has suggested that technology and networked learning have
contributed to this ‘radical shift in favour of the individual learner, where personali-
sation is considered to challenge the dominant view of the enclosed, mass treatment
by educational institutions’ (p. 407). Selwyn (2016) argues that while this focus on
the learner and learner choice is typically equated with giving control back to indi-
viduals, the reality is somewhat different. It rather emphasises the role of market
values and the positioning of learner as product and the packaging of education for a
consumer society with ‘its emphasis on self-expression and lifestyle choices through
individualistic acts of consumption’ (p. 79).
In this chapter, we position the learner within the discourse on MOOCs. We exam-
ine the motivations, learning dispositions and behaviours of learners and what the
research demonstrates as the best ways to support individual and collective learn-
ing journeys. We start by considering distinctive ways the learner is perceived by
different stakeholders.
3.2 Student, Learner, User, Participant—Multiple Names

for Multiple Actors
A range of terms have been used to denote people taking a MOOC: learner; stu-
dent; user; participant. Typically, they are employed uncritically and interchangeably.
Rarely are the terms or how they shape our understanding of the role, agency and
position of the individuals they name interrogated. Biesta (2009) suggests that what
we call those who are the subject of education matters. Not because language has a
particular power but because the use of a particular word leads more easily to other
words, and therefore becomes connected, often unconsciously, to certain meanings
and assumptions. Biesta (2009) emphasises the importance of the labels attributed
to those who are the receivers of education. What we call those who are the subject
of education matters. This is not only because language can be powerful, but also
because these labels are open to interpretation and could lead, unconsciously, to mis-
1 Todd Rose, The Myth of the Universal Learner Available from: https://www.vteducation.
org/en/articles/collaborating-technology-and-active-learning/myth-universal-learner-todd-rose-
variability.
3.2 Student, Learner, User, Participant—Multiple Names … 37
construed meanings and assumptions. For instance—if the MOOC learner is labelled
as a ‘student’, it may conjure images of someone who has signed up to complete a
course. The learner may also be considered a ‘consumer’ who would be willing to
pay a fee to participate in a MOOC. These terms, ‘student’ and ‘consumer’, signify
different values.
Language and the words we use determine what can (and cannot) be done and what
is (and what is not) possible. What we choose to label those individuals engaging in
a MOOC influences how we position these individuals in relation to each other, to
the teacher, to the content and instructional design, to the technology, to the platform
provider, to the outcomes that they achieve or attain.
The choice of language around individuals extends to further encapsulate the terms
used to describe different components of the learning journey. Successful completion,
engagement, interaction, learning, achievement, accreditation are all used to denote
the desired behaviour and to shape the methods of participating in the learning space
of MOOCs.
Traditionally, the subjects of any educational experience, or those belonging to any
educational system are unequivocally referred to as ‘students’ As students enrolled
in a programme of study at an institution (be it offline, online or in a blended setting),
there is consensus as to the overarching purpose of their engagement and activity,
and in many cases a relatively linear trajectory of their educational experience. The
student is positioned as the subject of education, the one who is summoned to study.
As a subject of education, they are situated as part of a formal, hierarchical educational
system, which has rules, regulations and outcomes that are externally determined.
3.2.1 The Student, the Learner
From the new language of learning perspective, the student is less subject than object,
lacking the agency to chart their own educational experiences or to shape their learn-
ing journey. They, however, operate from a position within the system and by virtue
of being a member of an established institution are offered a degree of legitimacy.
The extent to which an individual enrolled in a MOOC might be labelled a student
is contested. MOOCs can operate within or outside of established institutions, edu-
cational frameworks and traditional structures. And this fluidity in the positioning
of MOOCs and the considerable plurality in the agendas, motivations and goals of
individuals enrolled in them makes it challenging to position the learning experi-
ence of enrollees within traditional educational structures, and often the institution
providing the MOOC in which the student is enrolled.
‘Learner’ is increasingly used in formal and informal, online and offline learning
contexts. Part of its popularity is the notion that the learner is an active agent who has
control over and takes responsibility for their educational journey and in determining
their learning experience. Although the ability (or inability) of all learners partici-
pating in MOOCs to become active agents and determiners of their own learning
journeys will be explored in Chap. 4. The ‘student-led’ nature of learning is further
emphasised through the (desired) merging of roles between teacher and learner in
MOOCs. That is, MOOCs frequently position participants not only in the role of
students but also as teachers who are supposed to take responsibility for supporting
the learning and development of other participants. For example, in Chapter One we
described how ‘cMOOCS’ are designed such that students learn by contributing and
sharing knowledge within the MOOC network. Some MOOCs have peer-review
mechanisms, where students are expected to provide constructive feedback on
assignments, and projects. Alternately, within MOOC discussion forums, learners
voluntarily take up the role of being moderators, or Teaching Assistants. This idea of
social learning in a MOOC, where massive numbers of participants act as students
and, at the same time, teachers of others, has been underscored in Chap. 1 as one of
the most important features of MOOCs (Ferguson and Sharples 2014).
While this model of collaborative, socially constructed and collectively deter-
mined learning and the fluid movement between roles is, to many, an appealing
notion, its manifestation in reality is more questionable. Studies suggests that major-
ity of learners in MOOCs operate as isolated individuals (Hew and Cheung 2014),
firmly identifying with the role of learner, rather than taking responsibility to con-
tribute to the collective learning and knowledge building of all MOOC participants.
This may seem surprising because these notions of agency and self-determination
frequently are used to represent a liberation of the learner from traditional power
structures in education, from the dominance of the institution and a top-down edu-
cational approach where the teacher controls and determines the nature of the expe-
rience within the tightly controlled guidelines of the accrediting institution. Perhaps
the learner does not always want to be emancipated.
In traditional models of education the agenda is controlled by institutions who
determine the inputs, processes and outcomes of learning. Selwyn (2016) suggests
that connectivity of digital technologies has the potential to recast social arrangements
in education. Online learning is positioned in opposition to this apparent ‘top-down’
traditional model. He claims:
Such descriptions are intended to convey a sense of the mismanagement of education by
monolithic institutions that are profoundly undemocratic and archaic. These are lumbering
organisations where ownership, control and power are concentrated unfairly in the hands of
elites – be they vice chancellors and university professors, or school district superintendents,
tenured teachers and their unions. Like many large administrations and bureacracies, these
institutions that are believed to be unresponsive, incompetent, untrustworthy, ungrateful,
self-serving and greedy. (Selwyn 2016, p. 11)
The narrative of the broken system, and the transition of power and agency from insti-
tutions to individuals belie the common reality of a perpetration of existing models
in MOOCs. Selwyn (2014) warns that the reality is a continuation of the existing
hierarchy, from those that ‘do’ educational technology (traditional institutions and
the new-comers technology companies) to those who have educational technology
‘done to them’.
The term ‘learner’ has particular appeal in the context of MOOCs because of the
supposed potential of MOOCs to disrupt traditional tenets and structures of educa-
tion. Open and flexible enrolments result in diverse demographics which, in turn,
introduces a range of learner motivations and goals. This leads to highly variable
patterns of engagement both across MOOCs and often within the same MOOC.
Conole (2013) suggests that participation can range from completely informal, with
learners having the autonomy and flexibility to determine and chart their own learn-
ing journey, to engagement in a formal course, which operates in a similar manner
to offline formal education. Furthermore, the curriculum and content of a MOOC
is not always static, but incorporates (both by design and through differing modes
of learner engagement) a range of learning opportunities and pathways, which indi-
vidual learners are able to self-select and independently navigate. In contrast to the
relatively linear, pre-established standards of traditional education, MOOCs enable
individual learners to determine their engagement in relation to their self-identified
goals (DeBoer et al. 2014).
However, as will be explored in greater detail later in this chapter, the agency
that the term ‘learner’ endows can be problematic. Frequently, there is a disjunction
between the espoused and enacted position of the learner. That is, not all learners
in the MOOCs have the necessary knowledge, skills or dispositions to be an active
agent in their learning journey and consequently cannot engage in the opportunities
on offer in the same ways or for the same outcomes (Littlejohn et al. 2016). Equality
of access does not result in equal outcomes across learners.
While the term learner (and the structure of MOOCs), in theory, but frequently not
in practice, endows an individual with the agency to determine and chart their own
learning journey, Biesta (2009) warns that the term learner also denotes a lack. That
is, the learner is missing something that they must learn. The learner, therefore, is in
a position of inequality, until they have learned whatever it is that they need to learn.
In many ways, the positioning of MOOCs within the rhetoric of lifelong learning
and the continuous need to upskill reinforces the learner as deficient in someway.
MOOCs increasingly are targeting this deficit in individuals and positioning them-
selves as the cure and solution to it. Later in this chapter, in the section on ‘A closer
look at the role of self-regulated learning in MOOCs’, the implications for individual
learners of this deficit thinking combined with the agency and self-directed nature
of the learning experience in MOOCs will be explored in greater detail.
3.2.2 The User, the Participant
‘User’ is a term frequently used in discussions of technology. The meaning attached

to the expression ‘user’ is mutable. In certain contexts, it refers to people ‘using’
content resources, which in the context of MOOCs serves to emphasise the notion
of the MOOC as a product and learning as a commodity. This commoditisation of
learning plays into the neoliberal position of education. In certain contexts, user
may be used to convey freedom and agency to engage in the ways that best suit the
individual. In this sense, it references the democratising power of technology, which
can facilitate bottom-up activity by endowing individual users with the opportunity
and ability to engage, lead and construct their online activity. The user, in conjunction
with the educator or course developer, plays an integral role in the development and
continued innovation and evolution of a particular product or experience. However,
it equally may signify a closed and mechanistic use of the resources provided.
The term ‘participant’ serves to position the individual in an active role, and makes
implicit reference to the centrality of technology to the experience. As such, they
align with Siemens (2013) conception of the MOOC as a platform (rather than a
course), on which individual learners (or users or participants) define and construct
their own learning. Siemen’s vision elevates a constructivist model of learning and
knowledge over the transmission model in MOOCs. Thus, on a MOOC platform,
users can be defined as—People who are offered rights to create, add, modify and
disseminate content and knowledge through their interaction with other users and
technology.
However, while the terms ‘user’ and ‘participant’ indicate a shared approach to
learning where power and agency is distributed amongst all people involved in a
MOOC, regardless of their position as convener or creator and learner, the reality of
engagement in ‘connectivist’ learning environment (often referred to as cMOOCs,
see Chap. 1) is more complex. While the terms ‘user’ and ‘participant’ (on the surface
at least) afford agency to the individual actively to chart and determine the nature
of their engagement, providing an allusion of user-control, the reality is somewhat
different. Chapter 2 illustrated that cMOOCs, far from opening up education and the
nature of engagement, require people to behave in specific ways. They are founded
on everyone actively sharing and building knowledge, with each user or participant
responsible for the continual evolution of the MOOC (Knox 2016). As such, they
do not allow individuals to determine their own level of engagement. Passivity in
a cMOOC is equated with non-engagement and nonconformity to the ‘norms’ of
behaviour and learning (Milligan et al. 2013).
Yet the shifting language—student to learner, user to participant—suggests a
reorienting of power in education and learning, with individual learners or partici-
pants responsible for identifying their learning needs and the learning opportunities
that will be serve these. These individuals then moderate their behaviour and actions
in order to reach their self-determined goals and outcomes. This shift in power is
matched by a shifting of the role of learners. Ideally in a MOOC, every learner should
simultaneously exist as a teacher by contributing their unique skills and knowledge
back into the MOOC. However, many MOOC learners choose to learn individually
and in isolation and few take responsibility for teaching others (Hew and Cheung
2014; Milligan et al. 2013), which means that the reality is somewhat different to the
scenario suggested by the shift in terminology.
Feinberg (2001, p. 403) warns about this shift in power and emphasis on individual
learners determining their learning needs. According to Feinberg, the expert knows
best and the novice cannot make the decision about the pathway:
In market models consumers are supposed to know what they need, and producers bid in
price and quality to satisfy them. In professional models the producer not only services a
need, but also defines it /…/ Sam goes to his physician complaining of a headache. Is it an
aspirin or brain surgery that he needs? Only the doctor knows.
Social learning is an important characteristic of MOOCs. However, the plurality of

the terminologies used to denote those who participate in MOOCs is symbolic of
a shift away from ‘the social’ towards the ‘individual’. Students are now termed
learners and users are viewed as participants, symbolising the shift from what we
perceive as ‘education’ to what we understand as ‘learning’. This shift elevates and
emphasises the position of the individual and individual pursuits. Whereas education
is part of a broader programme, the aims and purposes of which we may or may not
support. Through this agenda, students are members of an institutional structure
and their socialisation within this structure becomes a pivotal part of their learning
experience. Yet, the MOOC often becomes a decontextualised space, where the
individual and the individual experience is emphasised.
3.3 Why a MOOC? Motivations and Incentives Among

MOOC Learners
The democratising rhetoric surrounding MOOCs is acknowledged by Biesta (2009),

who suggests that ‘[t]here are even emancipatory possibilities in the new language of
learning to the extent to which it can empower individuals to take control of their own
educational agendas’ (p. 38). While the language empowers, the reality is that many
learners do not have the cognitive, behavioural or affective characteristics necessary
to be active agents and determiners of their own learning pathways. Early critiques
of MOOCs suggested that they were not achieving their emancipatory aims but
rather were reinforcing existing trends and inequalities in participation in education
and learning. While this concern remains, there is growing evidence to suggest that
MOOCs are attracting a broader demography of learners, and that learners have a
broad range of motivations for engaging in a MOOC.
The open, flexible nature of MOOCs in theory—though not always in prac-
tice—enables individuals to determine with what, how and when they will engage.
As a result, learners in MOOCs typically have a wider range of motivations and
needs for learning than is normally observed in a conventional course or traditional
educational experience. The flexible structure of MOOCs, in which there are few
barriers and minimal formal consequences to learners ‘dropping in’ and ‘dropping
out’ of a MOOC, leads to fluidity in learners’ behaviours and actions (Yang et al.
2013).
The structure of learning in MOOCs, which typically involves minimal direct
interaction between the instructor and learners, places the onus on each individual
learner to determine and direct his or her own learning and to become teachers for
other learners. Learners are not only required to self-regulate their learning, and
to determine when, how and with what content and activities they engage, but they
further have autonomy over determining the outcomes of their learning. The ‘product’
of a MOOC is not standardised across all learners. Learners can set some of their own
terms of participation in MOOCs and therefore have a very different relationship to
Fig. 3.1 A video-based lecture in the Fundamentals of Clinical Trials MOOC
course requirements, learning processes, and often the institution offering the MOOC
compared with what occurs in traditional forms of higher education.
Research suggests that there is considerable variety in learners’ motivations for
enrolling in a MOOC (Littlejohn et al. 2016). Our own research on self-regulation
in MOOCs suggests that learners displaying higher levels of self-regulation were
more likely to conceptualise MOOCs as non-formal learning opportunities and to
independently structure their learning and engagement to best serve their self-defined
and self-identified needs (ibid.).
The Fundamentals of Clinical Trials MOOC (https://www.edX.org/course/
harvard-university/hsph-hms214x/fundamentals-clinical-trials/941) was run by the
Harvard University over 12 weeks in 2013 using the edX platform. The course
attracted 22,000 learners from 168 countries. The course was designed around a
weekly rostrum, with regular, video-based lectures, as illustrated in Fig. 3.1.
Aside the video lectures, learners had access to other forms of course content
including e-texts (Fig. 3.2).
Learners could interact through an online forum on the edX platform (Fig. 3.3)
and assessments were computer marked (Fig. 3.4).
A study of the ways learners self-regulate their learning in this MOOC has
previously been published (Milligan and Littlejohn 2016) and was compared with
approaches to learning in the Introduction to DataScience MOOC, described in
Chap. 4.
3.3 Why a MOOC? Motivations and Incentives Among MOOC Learners 43
Fig. 3.2 An e-text from the Fundamentals of Clinical Trials MOOC
A study of the ways learners self-regulate their learning in this Fundamentals of

Clinical Trials MOOC was compared with approaches to learning in the Introduction
to Darascience MOOC described in Chap. 4. Self-regulation is a fluid characteristic
that changes for each learner, depending on the context. Learners may be highly
self-regulated in one context and less self-regulated in another. Thirty five learners,
who perceived themselves as either a low or a high self-regulator, were interviewed.
Most learners who perceived themselves as poor self-regulators aimed to complete
the MOOC and be awarded the course certificate:
This class motivated me to do whatever was required to get the certificate … When I first
took the course I thought I would use the course certificate … to add to my LinkedIn profile.
I did do that. (LSRL, 783)
By contrast, learners who perceived themselves as highly self-regulated learners

reported they were interested in the MOOC because it could improve their work
performance:
The most important factor… is not even how much I learn, but how big the impact of my
work can be to the outside world. (HSRL, 119)
These motivations appeared to influence the learner’s actions, in particular how they
self-evaluated their learning and how satisfied they were with their progress. The high
Fig. 3.3 Fundamentals of Clinical Trials MOOC online forum
self-regulators who participated in the MOOC to improve their work performance

were strategic about where they focused their time and effort. When asked about
whether and how they followed the course pathway, high self-regulators responded:
[I tend to] follow what interests me and not worry too much about trying to keep a com-
plete overview of the area… I plan to complete all of the assignments[but] I won’t be too
worried if I don’t. (HSRL, 428)
Carefully curated parts… I’m going to be picking through what nuggets are of use to me in
particular contexts. (HSRL, 505)
However, learners who reported low self-regulation usually opted to follow the course
pathway, spending time on the course materials:
My goal is definitely to watch all the videos and the content provided and try to solve all
the assignments, although not necessarily I will try to take part in the additional optional
assignments. (LSRL, 603)
These learners tended to carry out most of the MOOC activities, in contrast to the high
self-regulators who were more strategic about where they focus effort. More time
was spent observing course materials, leading to difficulties with time management,
compared with high self-regulators.
Another advantage for high self-regulators was that, because they set their own
learning goals, they evaluated themselves against their own personal aims and were
more able to self-assess their progress. There was evidence that high self-regulators
Fig. 3.4 Computer marked, multiple choice assessment in the Fundamentals of Clinical Trials
MOOC
were self-satisfied with their progress, since they were readily able to identify their
own learning gains. This relationship between perceived progress and affective power
was explained as follows:
Now I’m feeling more powerful, I can do some things, I am confident in finding solutions
for problems that are too big for me right now. (HSRL, 670)
However, learners reporting low self-regulation experienced difficulty in self-

evaluating their progress. This is because these learners tended to follow the course
pathway and tried to self-evaluate their progress in relation to what was expected
of them by the course designers, which was difficult for them to estimate. When
questioned about self-evaluation, two respondents reported:
It’s hard for me to gauge how much I’ve understood something… sometimes we have a
blindness about it ourselves. (LSRL, 236)
Yeah that’s a difficult question because I don’t perceive my own learning. (LSRL, 396)
The second MOOC was Fundamentals of Clinical Trials, one of the first Harvard
University MOOCs. The course was developed by the Harvard Medical School,
Harvard School of Public Health and Harvard Catalyst and ran on the edX platform
from November 2013 until April 2014 with 24,000 registered learners from around
the world. The research design used the same method and instruments as used in the
Introduction to Data Science study and has been previously reported (Milligan and
Littlejohn 2016). Thirty learners located in various countries around the world were
interviewed.
Learners who reported high and low self-regulation described the same motivation
for participating in the MOOC: to gain a Harvard certificate. This finding is different
to the Data Science MOOC, where high and low self-regulators had different reasons
for joining the MOOC. The reason why there is a difference in this MOOC is not
clear, though gaining certification for professional development is more prevalent
in the health sciences than in data science. Another reason could be because of the
perceived value of a Harvard certificate.
However, even though high and low self-regulators had the same motivation for
participating in the MOOC, their approach to goal-setting and learning strategies
was different. Low self-regulators tended to follow the course ‘pathway’ set out by
the instructional designers:
I do download the study material which is provided by the course website, but while I watch
the video I do not have a habit of making notes and I am a person who is organised in a mess.
So even if I make a note I don’t recollect and read those notes. (LSRL, 295)
I’ve tried to go through the questions first and then go back and review the text to see…and
that forces me to kind of focus on the topics a little bit more as opposed to if I go to the
lecture and then try to do the questions I find myself zoning out during it. (LSRL, 360)
This behaviour is similar to the conduct of low self-regulators in the Introduction to

Data Science MOOC.
Learners who reported high self-regulation also reported behaviours comparable
with high self-regulators in the Data Science MOOC. These learners were strategic
about their learning task strategies and time management:
I don’t put too much effort into what I’m learning, but this course – looking at the videos
I get to take my time to understand. Sometimes I watch the video twice, which has really
helped me to have a better understanding when I’m learning. (HSRL, 284)
These data illustrate that high self-regulators strategically manage their time and
tasks. They select and engage in sections of a MOOC that support them meet their
own goals, whether to attain a course certificate or to learn specific concepts or skills
that they perceive as important. These learners may not appear to be engaged to
learning, yet they intentionally are being selective about what they learn.
Common factors that motivated students to learn include: interest in the topic,
access to free learning opportunities, the desire to update knowledge or to advance
professionally, the opportunity to engage with world-class university content and
the wish to gain accreditation and new credentials (Davis et al. 2014; Wintrup et al.
2015). Christensen et al. (2013) found that nearly half of MOOC students reported
their primary reason for enrolling in a course was ‘curiosity, just for fun’, while
43.9% cited the opportunity to ‘gain skills to do my job better’. While early engagers
with MOOCs were more likely to be interest-driven, and so-called ‘lifelong learners’
whose incentives tended to be more heavily weighted towards intrinsic or internal
factors, there is evidence that MOOCs increasingly are targeting the lucrative profes-
sional development market (Grossman 2013). They are learning for different reasons,
compared with undergraduates or ‘leisure learners’, and will be attracted by differed

sorts of incentives, such as learning specific knowledge to improve performance at
work or gaining a qualification.
MOOC platform providers and universities are introducing new incentive struc-
tures which mimic those commonly found in traditional education. For exam-
ple, credentialing is increasingly common among MOOC providers and courses
that provide some form of credential or institutional accreditation are the highest
growth areas (Shah 2016). In Chap. 1, we outlined how Coursera and Udacity have
launched their own credentials, offering what Forbes Magazine has termed a ‘badged-
future’, where accreditation is much more dynamic than in conventional educa-
tion (see https://www.forbes.com/sites/ryancraig/2015/09/30/coursera-udacity-and-
the-future-of-credentials/#300a92202b31).
There are other dramatic changes to education triggered by MOOCs. In a move,
which Shah (2016) has termed MOOCs as a ‘Netflix-like experience’, a number of
providers have responded to a demand from learners to have greater flexibility in
when and how they engage in a MOOC by moving from courses being offered at set
times during a year, to becoming self-paced and available continuously. This frees
the learner from having to start a course on a date determined by an institution to
beginning learning at a time that is convenient for them.
Mak et al. (2010, p. 280) suggestion that understanding learning in MOOCs
requires a ‘nuanced, strategic, dynamic and contextual’ understanding of individual
learners and individual MOOCs is remarkably apt. While there are lots of new benefits
on offer, it is not always clear how these help [all of] the learners.
3.4 But Who Benefits?
In a MOOC, learners are able to set their own terms of participation, which is different
from much of education where course objectives and learning designs are set. MOOC
learners have a very different relationship to course requirements, learning processes
even the institution offering the MOOC, compared with what occurs in traditional
forms of higher education. Biesta (2009) explains this in relation to the new language
of learning:
The absence of explicit attention for the aims and ends of education is the effect of often
implicit reliance on a particular ‘common sense’ view of what education is for. We have to
bear in mind, however, that what appears as ‘common sense’ often serves the interests of
some groups (much) better than those of others. (p. 37)
Indeed, we are witnessing that the design of MOOCs, the focus on the individual as
the primary unit, and the emphasis on the individual as active agent in their learn-
ing journey, is privileging those who can learn. Self-regulation, therefore, emerges
as a key lens for understanding nature of who is able to benefit from the learn-
ing opportunities offered in a MOOC. The wider context of a learner (rather than
the often-superficial dimensions of prior educational attainment, geographic region,
job) influences what they will get out of their learning journey. Selwyn labels this
‘inequalities of participation’ (2016, p. 31). That is, the experiences and outcomes
of a particular learning experience will differ considerably, depending on who the
person is.
Selwyn (2016) goes on to explain how a focus on equality of access without
corresponding understanding of the need to ensure equality of participation has led
to:
The assumption that all individuals can navigate their own pathways through digital education
opportunities implies a corresponding withdrawal of expert direction, guidance and support.
While offering an alternative to the perceived paternalism of organised education provision,
this approach does bump up against the widely held belief in education that learning is a
social endeavour that is best supported by more knowledgeable others. (p. 74)
Cottom (2014) argues that online systems get designed and configured to ‘the norm’
of a self-motivated, highly able individual who is ‘disembodied from place, culture,
history, markets and inequality regimes’. That is, MOOCs tend to cater for those who
have the social and educational capital to engage with the learning opportunities pre-
sented and furthermore, as briefly discussed in Chap. 1, MOOCs typically disregard
the offline context of the learner and how this might influence and shape both the
nature of their engagement and the outcomes they desire from their participation.
Without additional incentives, adults will not learn something that they are not
interested in or consider unimportant (Billett and Somerville 2004; Illeris 2007;
Siemens 2006). The choice to seek out and engage with both formal and informal
learning opportunities and the proclivity and ability to adopt and assimilate new
knowledge are determined by the individual. The experiences and interactions that
have occurred throughout a person’s life shape the values, beliefs, concepts and
approach that they bring to their future learning (Rogoff 1990; Scribner 1985). A
learner’s personal ontogeny mediates and is mediated by the contexts in which they
are situated and the orientation of their needs in relation to a particular learning
opportunity. Individuals actively seek out opportunities that they believe will grat-
ify the particular needs they have. The more gratification they receive, or expect to
receive, from their actions, the more they will continue to engage in the behaviour.
Conversely, negative outcome expectations lead to decreased engagement (LaRose
et al. 2001; LaRose and Eastin 2004). A theme that recurs in this book is that dis-
engagement is perceived as a significant problem in MOOCs, because few learners
complete courses relative to formal education. (Jordan 2015). However, the eman-
cipatory effect of free online access to education allows learner to take what they
need from MOOCs to meet their own learning goals without formally completing
courses, therefore completion rates can be misleading (LeBar 2014; Littlejohn and
Milligan 2015).
In Chap. 1, we explored the spectrum of instructional designs applied to MOOCs.
MOOC designs range from well-packaged content to open, networked designs. A
problem with almost all MOOCs, no matter how they are designed, is that they tend
not provide expert human feedback to learners, which means that the learners have
to pursue advice and criticism themselves (Margaryan et al. 2015). This focus on the
3.4 But Who Benefits? 49
individual taking responsibility for their own feedback and learning journey means
that those who benefit from MOOCs are the people who are best able to regulate
their own learning. As McCathy (2011) explains:
These discourses position the individuals as the locus of success or failure: based on their
self-discipline, hard-work, ambition, personality and efforts, they will either fail or succeed
procuring for their well-being …. Missing in these discourses is any consideration of the
differential and inequitable positions of subjects in terms of economic, social and cultural
capital, age, gender, class, race, ethnicity and sexual orientation. These discourses are based
in the assumption that all subjects are equally positioned to identify, mobilize, and create
productive and successful choices. (p. 303)
The next section examines how MOOC learners self-regulate their learning in
MOOCs.
3.5 A Closer Look at the Role of Self-regulated Learning

in MOOCs
Self-regulated learning provides a theoretical means for accommodating the diver-

sity in motivations and incentives among learners and the mutable, learner-driven
nature of the learning experience in MOOCs. Self-regulated learning refers to ‘self-
generated thoughts, feelings, and actions that are planned and cyclically adapted to
the attainment of personal goals’ (Zimmerman 2000, p. 14). In studies of formal,
offline learning contexts, Zimmerman (1990) suggests that motivation and learning
are interdependent processes and that individuals exhibiting higher self-regulation
are more proactive in their approach to learning.
Similar findings have been observed in studies of MOOC learners. Those learn-
ers identified as exhibiting highly self-regulating behaviour were less concerned
about outward measures of performance in MOOCs, preferring to concentrate on
developing knowledge and expertise that was relevant to their professional needs
(Littlejohn et al. 2016). That is, high self-regulators were more inclined to deter-
mine their own outcome measures rather than to rely on externally determined goals
or incentive structures to shape their engagement. This contrasted to learners who
exhibited lower self-regulated learning behaviours whose goals were more likely to
be tied to concrete, traditional and extrinsic measures of performance, for example,
completing all the assignments and earning a certificate of completion.
These findings align with research focused on offline learning which determined
that learners displaying high self-regulative behaviour are more likely to adopt ‘mas-
tery goal orientation’, structuring their learning around the development of content
knowledge and expertise (Zimmerman 1990). Pintrich and de Groot (1990) similarly
found that learners who considered their learning to be interesting and important are
more cognitively engaged than those learners who are motivated primarily by grades.
In research on MOOCs, those learners displaying higher levels of self-regulation were
more likely to conceptualise MOOCs as non-formal learning opportunities and to
independently structure their learning and engagement to best serve their self-defined
and self-identified needs. The motivations a learner brings to a particular MOOC,

together with the incentives structuring their engagement influences how they inter-
pret the role and purpose of the MOOC and the outcomes they seek, which in turn
shapes their behaviour and actions in the MOOC. As Illeris (2007) suggests, incen-
tives influence the ways in which learners engage with or acquire content. As the
following section will explore, this is not a monodirectional relationship. Content
and the pedagogical design of a MOOC also influences the acquisition process.
3.6 Learning Behaviour: Diversity in Engagement
While MOOCs emphasise the primacy of the learner and the role individual learners’
play in structuring their engagement, there has been a tendency in the literature on
MOOCs to focus on design solutions that encourage desired modes of engagement
and participation (see for example Guàrdia et al. 2013; Daradoumis et al. 2013).
These desired learning behaviours borrow heavily on metrics derived from tradi-
tional forms of education. That is, the ideal learner is one who adopts behaviours
that lead to the successful completion of a course and, where applicable, certifica-
tion and accreditation. Traditional measures of learning, such as passing tests and
assignments, and becoming accredited, continue to be the gold standard of successful
learning in MOOCs. So much so that many researchers, when exploring the impact
of different modes of engagement on a MOOC, continue to use completion as the
dependent variable. There is a debate in the literature to ‘reboot’ research on how
people learn in MOOCs by finding better indicators of learning in MOOCs (see Reich
2015).
Kizilcec et al. (2013) have developed a now widely accepted typology of four
profiles of learner engagement in MOOCs: (i) auditing—learners who did not do
the quizzes or assignments but engaged with other resources, such as the video
lectures; (ii) completing—learners who completed all of the activities; (iii) disen-
gaging—learners who participated at the beginning of a MOOC but whose engage-
ment dropped off or ceased over time; and (iv) sampling—learners who engaged in
resources once or twice, often in the middle of the course, but were not consistent in
their engagement. While there have been some attempts in the literature to suggest
that certain engagement profiles are ‘better’ than others, and indicative of greater
learning, there is limited evidence to back this up. Ideas around ‘good engagement’
tend to be based on the assumption that MOOC learners intend to complete courses,
akin to students in formal education courses (LeBar 2014). As we previously indi-
cated, MOOCs allows learner to learn what they need from the course and drop out
(Jordan 2015). MOOCs, therefore, have the potential to legitimise learning behaviour
that in traditional contexts would be characterised as deviant, non-learning, associ-
ated with failure.
There are a number of typologies of MOOC learners and each takes a different
perspective. For example, Milligan et al. (2013) identify different learning behaviours
in MOOCs; Clow (2013) defines learners according to their participation; Gillani
3.6 Learning Behaviour: Diversity in Engagement 51
and Eynon (2014) define learners based on their engagement in discussion forums.
None of these typologies examine learner engagement, even though taking part in
MOOC a is a characteristic of MOOCs and is distinct from participation in formal
education. We conclude this chapter with the construction of a new framework for
understanding and interpreting learning engagement. This framework, importantly,
does not make any attempt to suggest that any one approach is better or worse than
another. Similarly, it does not suggest that a learner will always conform to a single
approach.
Visible:
A visible learner is one whose presence and activity within a MOOC makes them
‘known’ by other learners. This may include participation and interaction in the
discussions, undertaking and where applicable completing tasks, assessments and
undertaking the activities required for certification.
Invisible:
These learners tend to be largely passive in their engagement in a MOOC. That is, their
presence and activity is not visible to other learners. They do not actively contribute
to discussion forum; however, they may read the posts an activity commonly referred
to as ‘lurking’. They rarely undertake activities and generally are not attempting to
complete the course in a traditional sense or to gain certification.
Formal/qualification oriented:
These are the learners who perceive MOOCs as a formal learning activity, tend to
treat a MOOC more like a traditional style of learning activity or course. These
learners are likely to be more concerned with accreditation and ensuring that they
‘complete’ the MOOC and are likely to structure their engagement to achieve this.
Informal/interest-oriented:
These learners are less likely to be concerned with ‘completing’ the MOOC and
are more interested in acquiring the knowledge and skills in the MOOC without
requiring the formal documentation that they have done so. They tend to be more
independent in their approach to learning, and able to identify the types of activities
that they need to complete to get the outcomes that they desire (mainly self-identified
and self-defined).
These variables position MOOC engagement in four distinct ways, as illustrated
in the typology in Fig. 3.5.
The four types of learners will be discussed in greater detail in Chap. 4, where we
sketch out narratives of the experiences of MOOC learners. These narratives make
clear the validity of a range of learning behaviours in MOOCs. As a precursor to the
stories of actual learners in that chapter, the four types are briefly described below.
The ‘conventional’ learner is one who is motivated to complete the course and
gain certification. These learners are sometimes referred to as ‘ideal learners’ because
their behaviour fits with what MOOC designers and facilitators believe to be optimal
Fig. 3.5 A typology of MOOC Learners
for course completion (even though this behaviour may not fit with the learners’
own objectives). They tend to follow a largely linear trajectory, engaging with the
majority of the content and completing the activities and assessments. Furthermore,
they are active contributors to the discussion forums, both asking and answering
questions, and consider collaboration with other participants a key part of the MOOC
experience.
The cautious student also has a goal to complete the course and as a result—sim-
ilarly to the ‘conventional’ student—will engage with the majority of the course
content and activities. However, they often are not as confident and at times struggle
to regulate their learning and to select the best learning approaches for their needs.
Furthermore, they typically are reticent to post to discussion forums, though they
may read the contributions of others.
The invisible learner is motivated by a desire to learn, rather than to receive
accreditation or to complete a course. They often are highly regulated and are able to
carefully match their engagement to their needs and motivation. Their behaviour may
perfectly fit their own learning objectives, but is not ‘ideal’ for the course facilitators
or even for the other learners. They may be passive in their engagement and driven
by a desire for content and skills. Consequently, they typically do not undertake the
activities or assessments and do not contribute to the discussion forums.
The socialiser, analogous to the invisible learner, is not motivated by a desire to
complete the course or the prescribed activities. They similarly are able to chart their
own engagement with confidence. They may undertake some activities. However,
their preliminary focus is collaborating with other participants, by contributing to
the discussion forums.
MOOC participants tend to align with these learner types, depending on their
motivations, and may span different types, rather than falling into one single category.
3.7 Concluding Thoughts 53
The rhetoric around MOOCs has stressed their democratising potential, creating a
vision of the emancipated learner, who is no longer reliant on traditional institutions
and the barriers—financial, geographic, admission requirements—that they can pose.
While the language frequently employed suggests a reorienting of power in education
and learning, and elevating the role of the individual learner, it belies the responsibility
that comes with this new role. As this chapter has shown, the learners in MOOCs are
incredibly heterogeneous, with diverse motivations, goals and learning needs. The
four learner types discussed in this chapter will be explored in greater detail in Chap.
4, as well examine the diverse ways in which massive numbers of people learn in
MOOCs.
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Chapter 4
Massive Numbers, Diverse Learning
Abstract MOOCs provide education for millions of people worldwide. Though it

is not clear whether everyone can learn in a MOOC. Building on the typology of
MOOC participants introduced is in Chap. 3, and we explore the claim that MOOCs
are for everyone. We trace the different reasons people participate in MOOCs and
the ways they learn. MOOCs tend to be designed for people who are already able
to learn as active, autonomous learners. Those with low confidence may be inactive.
However, even learners who are confident and able to regulate their learning experi-
ence difficulties if they don’t comply with the expectations of the course designers
or their peers. For example, if a learner chooses to learn by observing others, rather
than contributing, this behaviour can be perceived negatively by tutors and by peers.
This indicates that MOOCs sustain the traditional hierarchy between the educators
(those that create MOOCs and technology systems) and the learners (those who use
these courses and systems). Although this hierarchy is not always visible, since it is
embedded within the algorithms and analytics that power MOOC tools and platforms.
4.1 Learning in MOOCs; What Does It Mean?
MOOCs have massive number of learners with diverse intentions and characteristics.
Yet, little is known about how and why they engage in MOOCs. Research on learning
in MOOCs tends to focus on MOOC designs, the data trails of learners and the
semantic traces they leave in discussion forums (Gasevic et al. 2014). These studies
tell little about the cognitive and affective factors that influence the reasons that
learners study, their learning strategies, why they drop in and out of courses and
whether they have learnt. Few researchers examining learning in MOOCs have taken
a holistic view of learners’ experiences, for example, by gathering learners’ stories
and listening to them describe their motivations, experiences and feelings about
learning in a MOOC. Yet an all-inclusive view is needed to allow critical analysis that
positions learning and technology within broader organisational, political, economic
and social contexts in order to explore how it can foster, support and counteract issues
of empowerment, equality and democratisation (Selwyn 2010).

https://doi.org/10.1007/978-981-10-8893-3_4
58 4 Massive Numbers, Diverse Learning
This chapter is informed by a programme of research overseen by one of the

authors which was based around conversations with 88 learners in three different
MOOCs (see Littlejohn et al. 2016; Milligan and Littlejohn 2016; Milligan et al.
2013). This research was motivated by the claim that MOOCs are opening up edu-
cation, which is underscored by the assumption that MOOC learners are able to
self-regulate their own learning. Our findings questioned this claim, highlighting
that MOOCs open up education principally for people who are already able to learn.
Our findings contest the belief that MOOCs challenge existing models and paradigms
of education. In fact our research illustrates that MOOCs are, in some ways, reinforc-
ing traditional patterns and behaviours in both learning and learners. The pluralism
that characterises the need for learners to be able to learn actively in MOOCs and
the limited ability of many MOOC learners to self-regulate their learning makes
any attempt to discuss MOOCs in a unified manner challenging. Furthermore, the
absence of strong, extant theoretical frameworks for conceptualising learning in a
digital age further limits the academic scholarship in this area.
Building on the typology of learners presented at the end of Chap. 3, this chapter
re-examines the potential to reconceptualise learning and learners in MOOCs, while
simultaneously questioning how much of this reconceptualisation is current reality,
versus a desired future vision. The pluralism present in the structure and purpose of
individual MOOCs is matched by the multiplicity of stances and approaches adopted
in this chapter. While, to the academic purist, moving between different theoretical
framings in a single chapter may be criticised, we argue that this multiple framing
aligns perfectly with the diverse frameworks governing the approaches to learning
in individual MOOCs and diversity of backgrounds, motivations and behaviours of
MOOC learners.
MOOCs frequently are positioned as re-operationalising traditional concepts in
education, representing a new approach to instruction and learning (Fischer 2014).
In Chap. 2 we characterised how the MOOC platform providers, along with their
university partners, have emphasised a re-orientation of learning through open access
to courses that are free of charge, use learning materials created by elite faculty and
facilitate interaction with thousands of other learners. At the same time, those who use
the ‘connectivist’ approach to MOOCs argue that the idea of learning in an open and
autonomous network changes the educational paradigm (Downes 2012). While this
is undoubtedly true in some cases, the degree to which they are re-operationalising
and reconceptualising the learning process requires careful consideration. MOOCs
hold an uncertain space where they appear simultaneously to challenge traditional
approaches and paradigms, while continuing to draw on and replicate existing edu-
cational and learning models.
To explore this tension between novelty and continuity in MOOCs, we draw upon
Illeris’ (2007) fundamental processes of learning framework as a lens for examining
the nature of learning. More particularly learning framework can be used for con-
sidering the positioning of the individual learner in relation to their broader MOOC
experience. Illeris suggests that at its most basic, learning requires two simulta-
neously occurring processes: (1) external interaction between the learner and their
social, cultural and material environment(s), where their activities and actions are
4.1 Learning in MOOCs; What Does It Mean? 59
situated; and (2) the internal, psychological process of acquisition and elaboration,
where new stimuli are connected with prior learning. These internal processes are
mediated through the individual, arising from the interplay between the incentives
influencing and structuring an individual’s behaviour, and engagement with content
and learning activities.
Put more simply, to understand any learning, it is necessary to consider how an
individual learner draws upon his or her existing cognitive frameworks, personal
ontologies and social capital to navigate the experiences, resources, tools and spaces
made available to them. How is the learner and his or her learning activity situated
within their broader contexts of action?
Illeris (2007) states all learning involves three dimensions: cognitive (knowledge
and skills), affective (feelings and motivation) and social (communication and co-
operation), which are embedded in the learning context (in this case the MOOC).
Thus, Illeris’ model combines the internal psychological stance of the individual,
with the socially mediated dimensions of the learning process.
Therefore, to understand the nature of learning in MOOCs, it is necessary to
consider how the internal drive to learn is transformed into learning opportunities
through an individual’s engagement with the socio-cultural and socio-technical con-
texts of practice. In these contexts learning is distributed across the individual, other
people, resources, technology and physical contexts (Cobb and Bower 1999; Greeno
et al. 1996; Pea 1997; Putnam and Borko 1997). Learning is embedded within the
individual’s cognition, influenced and shaped by their personal histories, as well as
situated in the environmental, social and technological contexts in which the indi-
vidual operates. Learning is explored through individual learners’ interactions with
online systems, with other people and with (online and offline) information resources
(Abeer and Miri 2014). Therefore, learners are influenced by their own cognition and
experiences, their social surroundings and both the digital and physical contexts in
which the learning is embedded.
Eraut (1994) suggests that learning does not occur when an individual encounters
an idea or information, but rather through new input or use. It is through being
enacted that an idea gets reinterpreted and acquires new meaning, which is specific
to the individual and their context. This moves beyond the learning as acquisition
metaphor (Hakkarainen and Paavola 2007; Sfard 1998) to the conceptualisation of
learning as construction (Piaget 1964). Hakkarainen and Paavola (2007) suggest that
in this conception:
Learning is seen as analogous to innovative inquiry through which new ideas, tools and
practices to support intelligent action are created and the knowledge being developed is
significantly enriched or changed during the process.
Learning, therefore, occurs within the internal, psychological setting of the individual
(thinking) as well as through the actions of an individual, (behaviour), which are
situated within a particular environmental context (Illeris 2007).
This reading of learning in MOOCs is in contrast to much of the literature, which
characterises MOOCs as de-contextualised learning experiences. MOOC platform
providers view MOOCs as contained courses supported by distributed and frag-
mented technology tools, rather than as a holistic learning journey that brings together
all the experiences and contexts each individual learner engages within (Ebben and
Murphy 2014). To more fully understand the nature of the learning experience it is
necessary to situate the MOOC, the learning opportunities it provides, and individual
learners within the multiple ecosystems in which they interact.
From this perspective, learning is not prescriptive or predefined by a set of objec-
tives. While the curriculum and learning outcomes of a particular MOOC may guide
the discourse and activities of the learners, the specific knowledge and concepts that
are learnt will emerge through the activities and actions of the learners, and will,
therefore, be influenced by a myriad of factors (Milligan et al. 2013; Williams et al.
2011). These factors encompass the understanding and experience the learner brings
to the course, including their motivation and level of confidence, the knowledge of
other learners, the course design, and the temporal and geographic contexts in which
the MOOC and its learners are situated.
4.2 Individual-Level Factors
A number of studies have sought to identify the individual-level factors that influence
successful learning in MOOCs. A learner’s geographic location affects not only
accessibility to MOOCs, but also their interest in topics (Liyanagunawardena et al.
2013), with demographic information positioned as a mediating factor to explain
behaviour in a MOOC (Skrypnyk et al. 2015). Confidence, prior experience and
motivation (Littlejohn et al. 2016; Milligan et al. 2013), and a learner’s occupation
(de Waard et al. 2011; Hood et al. 2015; Wang and Baker 2015) further have been
found to mediate engagement. A relationship between learners’ goals and their
learning outcomes has also been identified (Kop et al. 2011; Littlejohn et al. 2016),
while there is also evidence that a learners’ prior education experience influence
their retention in a MOOC (Emanuel 2013; Koller et al. 2013; Rayyan et al. 2013).
Some of these individual-level factors identified in the literature are associated
with the norms and expectations of how learners behave in education. Other factors,
raised in Chap. 3, are focused around the role of motivations, incentives and self-
regulation in determining how a learner engages within the learning environment.
4.3 The Environment
Learning is enabled in part through an individual’s participation within their con-

text of practice, as well as through interaction and engagement with the resources
(material and human) available in that context (Lave and Wenger 1991). The learning
process and resultant knowledge is shaped by the context(s) in which knowledge is
acquired and used. Nonaka and Toyama (2003) utilise the concept of ba, to explain
the specific context, encompassing both spatial and temporal dimensions, in which
4.3 The Environment 61
learning takes place and knowledge is created. Ba is a shared space for emerging
relationships composed of physical (classroom, office, etc.), virtual (digital tools,
platforms) and mental (concepts, ideas, shared knowledge) dimensions.
The environment is not a single, static entity but rather is comprised of multiple
complex systems, which come together to inform and shape the ways in which a
learner engages with learning opportunities and resources. Barron (2006), in her
work on learning ecologies, describes the importance of understanding the multiple
environments in which technology-enabled learning occurs:
Understanding how learning to use technology is distributed among multiple settings and
resources is an increasingly important goal. The questions of how, when, and why adolescents
choose to learn are particularly salient now, as there has been a rapid increase in access to
information and to novel kinds of technologically mediated learning environments such as
online special interest groups, tutorials, or games.
It has become easier for those with computer access to find resources and activities that can
support their learning in their own terms. However, there are also widespread concerns about
equity. Although physical access to computing tools is becoming less of an issue, there are
still stark differences among children and adolescents in access to learning opportunities that
will help position them to use computers in ways that can promote their own development.
In addition, there is the related concern that we convince a more diverse set of people to
pursue advanced knowledge that will position them to work in technological design fields.
(p. 194)
Barron goes on to explain that:

The survey responses indicated that often learning was distributed over several settings
and across many types of resources. More experienced students accessed a greater number
of resources both in and out of school. Individual differences in the range and types of
learning resources utilized were found even when physical access to computers and to the
Internet were the same, suggesting that differences were due to variations in interest or
resourcefulness. The results also suggested critical interdependencies between contexts.
(2006, p. 196).
Therefore, to fully understand the learning that occurs in a MOOC it is necessary to

understand both the individual learner, as well as how the learner is situated in and
navigates the multiple spaces, contexts and settings in which they and their learning
are situated and the materials and resources on which they draw.
4.4 Analysing the Norms of Behaviour
As Chap. 3 investigated, identifying a single ‘norm’ of behaviour or type of engage-

ment in a MOOC is impossible. MOOCs, at least in theory, are positioned to endow
learners with the flexibility to determine and chart their own individual learning
journeys. Consequently, learning cannot be understood without deep engagement
with the experiences of individual learners. That is, learning is inseparable from the
personal histories and experiences, beliefs, and motivations of individual learners as
well as their broader socio-cultural context and the relationship between the MOOC
and their offline contexts. It is difficult to know whether someone has learned unless
all of these factors are taken into account. Narrative accounts of learning provide the
sorts of qualitative data needed to understand whether a learner is learning. However,
these data are difficult to analyse and draw conclusions from.
To get around this problem and simplify analyses of learning in MOOCs, there has
been an emphasis on identifying digital trace data that can be analysed to monitor
academic performance. The greater the number of learners who provide data, the
larger the potential to analyse data in meaningful ways and provide scaffolds and
supports for learners.
Learning analytics usually is designed around one or more of the following:
• early alert systems that predict the likelihood of a learner falling behind or dropping
out of a course;
• visualisation systems that provide dashboards to tutors and learners illustrating
progress in relation to a pathway pre-prescribed by the tutor or in relation to the
learner’s position within a network of peers and tutors;
• recommender systems that endorse resources, people or future pathways;
• adaptive learning systems that aim to personalize the resources, people or future
pathways the learner accesses, depending on their demographics or progress.
Early alert systems are based on predictive analytics that predetermine the learner’s
likelihood of achieving a ‘success’ measure, by comparing the learner’s data to those
of other students. For example, systems have been developed to analyse contributions
to discussion forums and use these data to predict the likelihood of a learner dropping
out (Muñoz-Merino et al. 2015; Skrypnyk et al. 2015; Vu et al. 2015). Learners’
engagement and progression in a MOOC has been linked with a learner’s prior
education level (Rayyan et al. 2013). Jiang et al (2014) found factors related to a
learner’s behaviour in week 1 of a MOOC to be early alert indicators that signal
whether or not a student would complete the MOOC. These factors included the
number of assessments completed by the learner and the score from quizzes within
the MOOC. Other early alert indicators link time management in a MOOC and
retention (Balakrishnan and Cooetzee 2013). Retention rates have been correlated
with a lighter workload, higher autonomy and more flexible assessments; the highest
levels of perseverance were connected to autonomy, high levels of learning support
and scaffolding activities (Skrypnyk et al. 2015).
Visualisation systems include Social Network Analysis techniques that use the
learner’s position within a learning network as an indicator of his or her connect-
edness, assuming a relationship between the learner’s position in a learning net-
work associated with a MOOC and the likelihood of them leaving the course (Yang
et al. 2014). The learner’s position within this network may be strengthened through
interactions with peers and tutors using social media tools such as blogging and
microblogging tools or by linking with others through discussion threads (ibid.).
Other visualisation methods combine learning characteristics data with cognitive
and behavioural data. For example Buckingham–Shum and Deakin–Crick (2012)
link data on student’s ability to self-direct their learning with assessment data to
4.4 Analysing the Norms of Behaviour 63
feedback to learners how they might amend their learning in ways that allow them to
achieve success. Other, similar systems use recommendations, for example advocat-
ing that learners with a similar profile took a specific course of action (e.g. reading
a text or engaging in a supplementary course) to achieve success.
Recommender systems offer MOOC learners all kinds of guidance, including
advice about the next MOOC they select, or the likelihood of successful completion
of a course. These recommendations are based on different kinds of data gathered
from the learner and analysed against previous data from earlier rounds of the course.
For example Skrypnyk et al. (2015) reported how analysis of learners’ demographics
and cultural groupings allowed personalised recommendations to students about the
actions they could take to scaffold their learning. Emerging analytics systems are
gathering a wider range of data, including affective data that indicate how learners
feel about their learning. These data allow for more influential recommendations and
adaptations of learning resources.
Adaptable systems include MOOCs where content is tailored and personalised
for each student (Tabba and Medouri 2013). Some techniques adapt the learning
design of a course, depending on the data (Mor et al. 2015). Other systems use
semantic analysis of online discussions in MOOCs to allow adaptation. Gillani et al.
(2014) examined the strategies of hundreds of learners as they engaged in online
discussions. Using complex network analysis techniques, they identified a number
of ‘significant interaction networks’ embedded within discussion forums. Although
these interaction networks can support learning, they are vulnerable to breaking
down. MOOC providers are capitalising on these analytics techniques to structure
discussion forums so that students who join the course late are as able as the early
cohorts to form lasting bonds and get integrated into the cohort of students taking
the course.
Earlier, we indicated that learning is inseparable from the learner’s personal expe-
riences, beliefs and motivations, but data around these factors is difficult to measure
and analyse. As a shortcut measure, it is sometimes assumed that ‘learning’ is syn-
onymous with active engagement in a MOOC and with retention, completion and
certification (see, for example Hew 2014). An example is a study by Colvin et al.
(2014) analysed learning in the 8.MReV Mechanics ReView MOOC, offered on the
EdX platform from June to August 2013. The course, an introduction to Newtonian
Mechanics, was run in parallel with an on-campus course at MIT. The MOOC version
of the course substituted face-to-face lectures with video lectures and textbooks with
digital texts, and was open to anyone who met a number of prerequisites. The course
design was structured around weekly video lectures to help students engage with task-
based problem. The learning gains of 1080 students were evaluated by analysing the
results of pre- and post-tests through normalised gain and item response theory. The
learning gains for these students were comparable with those in the on-campus class,
and 95% achieved the MOOC certificate. However, unlike the campus-based course,
most of the MOOC students (almost 16,000 of the 17,000 people registered for the
MOOC) did not complete the course and achieve the certificate.
This example illustrates that as technology advances, MOOC providers need to
rethink MOOC models, and the role that tracking can play in them. Gathering data
is likely to be more streamlined into online learning and those data that are easiest to
measure are often used more prominently in analyses. However, one issue to consider
is whether the right data is being gathered (Gašević et al. 2015).
At times the application of analytics overlooks the fact that technology is socially
constructed and negotiated, rather than imbued with predetermined characteris-
tics (Gašević et al. 2015). Poor application of analytics may promote a narrow view
of desired outcomes and norms of behaviour in a MOOC which belie the fluidity and
flexibility of the learning opportunities that MOOCs can offer.
Over-reliance on learning analytics for understanding and measuring learning
may lead to what Biesta (2009) has termed ‘normative validity’. That is:
The question whether we are indeed measuring what we value, or whether we are just
measuring what we can easily measure and thus end up valuing what we [can] measure. (p. 35)
There is a danger that, by missing the learner’s context, that analytics systems may
oversimplify how we understand learning. There are three key problems. First sys-
tems may focus on data that are easily measured—retention, completion and cer-
tification, rather than what cannot be easily measured—learner motivations, goals,
self-regulation and agency—but are nevertheless critically important to learning. Sec-
ond, those who code the algorithms that underpin analytics may not be concerned
with the wider questions of the learner’s context and consequences for their learn-
ing decisions (Morozov 2014). The Joint Committee of the European Supervisory
Authorities has undertaken a consultation on big data and the financial profiling of
customers, emphasising that the algorithms that are used in big data analytics must
be shown to be unbiased, otherwise the benefits of analysis will be diminished (ESA
2016).
While learning analytics provide the potential to personalise the learning experi-
ences and opportunities of learners in MOOCs, the extent to which they can currently
do this is questionable. Selwyn (2016) suggests that rather than personalising the
learning experience, analytics instead is reinforcing mass customization of educa-
tion through large systems. He explains:
Many personalised, bespoke learning systems are concerned primarily with delivering prede-
termined content to students, albeit in different sequences and various forms of presentation.
(p. 72)
Learning analytics in MOOCs may “personalise” the learning but this “personalisa-
tion” is not to the individual needs or goals of the learner but rather to the behavioural
norms and desired outcomes of the MOOC provider. The learner’s behaviours are
being adjusted to maximise the outcomes for the course providers, rather than the
learning being optimised to meet the learner’s needs and objectives. This is because
the assumptions that underpin the analytics may be based on the MOOC provider’s
requirements, rather than the learners’s aspirations.
Algorithms are developed by coders to analyse data in a meaningful way. These
can be helpful in understanding data, but inevitably are shaped by underpinning
assumptions and biases. Data gathered and analysed by algorithms are limited by
the expertise and assumptions held by those people who write the code (Williamson
4.4 Analysing the Norms of Behaviour 65
2015). If the coders do not appreciate the underlying assumptions of their codes,
then the data the algorithms analyse can be compromised. According to Boyd and
Crawford (2011):
As computational scientists have started engaging in acts of social science, there is a ten-
dency to claim their work as the business of facts and not interpretation. A model may be
mathematically sound, an experiment may seem valid, but as soon as a researcher seeks to
understand what it means, the process of interpretation has begun. This is not to say that all
interpretations are created equal, but rather that not all numbers are neutral.
Researchers such as Williamson (2015) warn that these biases may result in a hierar-
chy between those that create MOOC systems and those who use these courses and
systems, such that empowered ‘producers’ of technical systems indirectly can overly
influence and exploit the student consumers of the systems. To aim for equality, we
need to engage with and interpret the qualitative narratives of individual MOOC
learners.
4.5 Qualitative Narratives and Learners’ Stories
Inequalities persist even for those people who do get to take part. In particular, experiences
and outcomes of education differ considerably according to who someone is – what is often
referred to as ‘inequalities of participation’. (Selwyn 2016, p. 31).
Engaging with the qualitative narratives of individual MOOC participants enables a

richer perspective of what it means to learn in a MOOC. An example of a MOOC
where we have gathered narratives of how learners have learned is Introduction to
Data Science (https://www.coursera.org/course/datasci). This MOOC was offered
in 2014 by the University of Washington on the Coursera platform. The course was
designed for people with a moderate level of programming experience. Over 8 weeks,
50,000 learners, from 197 countries participated in the course.
The course homepage, illustrated in Fig. 4.1, provided information about the
course aims and instructional design.
To achieve the course aims, learners were expected to engage in a number pro-
gramming activities, supplemented by educational materials including video lectures
(Fig. 4.2).
Learner interactions were enabled through sharing data science examples (see
Fig. 4.3), uploading assignments, engaging in online discussions within the MOOC
platform as well as collaboration through other social media sites, including
OpenStack, an online site commonly used by computer scientists to share codes
and discuss coding problems. Through creating and sharing computer codes, the
learners independently structured informal learning and combined this with the
formal learning activities within the MOOC. This ability to personalise learning
outcomes was important for professional learners who wanted to align their learning
in the MOOC with their job.
Fig. 4.1 IDS MOOC Introduction Page
Below are the narrative stories of the four types of MOOC learner outlined in
the typology in Chap. 3. These portraits are drawn from the stories of actual learn-
ers, who participated in the Introduction to Data Science MOOC. These narratives
are part of a larger study examining the self-regulated learning of 788 participants
in the MOOC (https://www.coursera.org/specializations/data-science). Quantitative
data was collected through a survey posted on the course message board. Participants
who completed the survey were invited to participate in an interview to explore their
experiences. 32 learners were interviewed via Skype. Their narrative accounts of
being a MOOC learner demonstrate the diversity of motivations, goals, learning
behaviours and perspectives of the participants.
4.5 Qualitative Narratives and Learners’ Stories 67
Fig. 4.2 IDS MOOC Video Lecture
Fig. 4.3 IDS MOOC Forum for sharing coding examples

The invisible agent

It’s very important for me to improve my knowledge base because I want to
ensure that I am keeping up to date with the latest ideas and thinking. The
MOOC is related to my profession. But I did it, not because I had to, but
because I was interested in expanding my knowledge and my skill set.
I’m a fairly independent learner and feel like I am good at knowing what I need
to do in order to learn the content and skills that I want to learn. I have the
strength of quickly being able to tackle the problem and search for results on
Internet sites, you know Google, forums and things like that. So, I think I have
that strength where I can quickly just go ahead. And I did this in the MOOC.
I didn’t tend to go through all of the activities or watch all of the videos. I just
picked and chose the content and activities that I thought were going to help
me the most. I also was very happy to go and find the information elsewhere.
When I need to learn something, I will usually try to do it myself, usually with
the help of Google and textbooks rather than to seek out another person or to
find a formal training opportunity. I have used those kinds of 3 avenues. I rely
a lot on academic literature for things of a technical nature and I also buy a
lot of books. So, I buy a lot of programming books, a lot of statistical, data
science and data mining book.
I guess one thing is I am optimistic, so it means I’ll try a lot of things and I kind
of enjoy doing new things and that makes it I guess kind of easy for me to go
out on a limb and do a whole bunch of different things and see how it goes. I’m
pretty decent at…basically I work reasonably well without the interaction of
other people. I didn’t particularly use the direction of other people during my
regular university classes or regular school classes and I don’t particularly
need it now. So, I guess it could be considered a strength, I don’t really need
to depend on other people for it.
The socialiser
The MOOC is more of a personal curiosity than a real work requirement. I’m
doing it for myself. Work know that I’m doing it, but it’s not a recommended
thing on the company, so I’m doing it out of interest.
I think that the way I wanted to approach the MOOC was just to follow
what interested me, and not worry too much about trying to keep a complete
overview of the area. I wanted to find appropriate tools, and tools that can be
used in a timely manner. I still completed a couple of the assignments, but I
wasn’t that worried that I didn’t keep going right to the end. To be honest the
assignment is not the best benchmark to measure your learning, it is one form
of measurement, but it’s not a huge one because a lot of times the assignment
is just a subset of what you do. Your peers are your best reflection actually.
4.5 Qualitative Narratives and Learners’ Stories 69
So, if you have someone who is doing the same thing and you talk to him or
her every day, then that’s the best thing actually.
I would say I now very rarely watch lectures. I will look through the slides and
I will read the transcripts that are provided, the subtitles, as a high-speed way
to look over the material. Then, if it isn’t obvious from those two, I’ll go to the
lecture and only then. But I’ve found it a much more effective way of learning
for me. I had realised that the discussion aspects were among those that suited
me best because, as I saw it, I could read a book and get the same content or
at least I could get equivalent content, I could watch YouTube videos and the
same kind of thing. The things that were really different were the motivation
from doing things with a group of people and the chance to talk things out
about issues. In my personal experience, being able to talk things out has been
really useful to me. So that’s probably the predominant way I learn in MOOCs
now.
The “conventional” learner
I was aiming to get a certificate of completion and to get a passing distinction
grade out of the class. I took the course very seriously from the beginning
and this meant that I planned to watch all the videos and go through all the
assignments. I have at least completed all the compulsory assignments.
I’ve taken several MOOCs and I would say that I’m at the point now where
I am very familiar with the platform and how to learn on a MOOC, at least
in terms of what works for me. So I can tackle courses very efficiently when
I’m doing them as a student. First of all I watch lectures and after that I try
to answer all the quizzes and questions, and after that I go to programme
assignments.
If there is a quiz which actually makes you think it generally drives you to read
more things, to discuss with your friends and generally helps you build your
knowledge a lot.
I made a little Excel spreadsheet with the key dates. So, for example, I knew
an assignment had to be handed in on a certain day or I knew a quiz had to
be handed in on a certain day, or I knew a course project had to be handed
in on a certain date. So then I guess I sort of kept track of what lectures I’d
need to have covered before I could answer those questions and I kept that in
mind. So I kind of planned my way through it, so I didn’t miss any of the hard
deadlines.
I think that the forums are very important because all the classmates could
have the same problems that I have and I think the forums are very important
for all the courses. When I’m working on a quiz or an assessment I like to go
into the discussion forums. And it’s the collaboration around the assessments
that I will get involved with on the forums. This is the type of collaboration on
the discussion forums that I will get involved in.
The cautious student
We’ve got a bit contract with the health service and that’s coming to an end
now, so they’re trying to move all out skills into a different area, so we’ve been
encouraged to learn a new database technology like NoSQL, analytics and so
this course just fitted that learning requirement. I hadn’t done any professional
learning for a couple of years, although I always feel I try and learn every
day if possible, but I hadn’t done a course with coursework for at least 5 or 6
years.
My primary goal is not to learn, but to complete the course so I can get certified
statement of accomplishment. So I definitely set out to watch all the videos
and the content provided and try to solve all the assignments, although not
necessarily to take part in the additional optional assignments. I am motivated
by the reward of getting a certificate. But my learning strengths? I don’t think
I have anything particular on this one. I always think if I start something then
I finish it. So I just want to keep this up.
I’m a designer so I find picking up a new thing is not that difficult, but it takes
time to really be good at it, to be comfortable with it. Some of the assignments
were quite a challenging task for me and I had to spend 3 days on one of the
assignments. It took me quite a bit of time. Sometimes it’s hard for me to gauge
how much I’ve understood.
I watched the lectures and then I did the assignments and if I found something
that I didn’t know, but it was really specific to the language, let’s say Python
function names, then I Googled. I didn’t talk to anyone. I occasionally went
onto the forum to read, but I didn’t ask questions on the forum. I mean it was
mostly general chit chat, but if I had a problem I’d do a search on it and then
it’s just a matter of looking through all the responses, trying to find answers
to problems.
4.6 Making Sense of the Learner Stories
One of the most impenetrable features of a MOOC is the variability in the degree to
which learners engage in the course. Analysis of publically available data on MOOCs
shows a positive correlation between course length and total number enrolments, but a
negative correlation between course length and completion (Jordan 2014). However,
as learner stories one and two above demonstrate, not completing is not synonymous
with not learning.
4.6 Making Sense of the Learner Stories 71
At the same time completion, or at least engaging with all of the content and
participating in learning activities, is not necessarily indicative of learning or of
the learner’s ability to participate in a MOOC. As learner story four (the cautious
student) illustrated, this individual was less concerned with learning, and, indeed, at
many stages struggled to regulate their learning behaviour and actions to maximise
their experience. Instead, this learner was motivated by a need, imposed by their
workplace, to undertake professional development.
The potential perils of MOOCs and online learning, and their inability to ade-
quately support the learning of all students is identified by Selwyn (2016) who
contended:
The assumption that all individuals can navigate their own pathways through digital education
opportunities implies a corresponding withdrawal of expert direction, guidance and support.
While offering an alternative to the perceived paternalism of organised education provision,
this approach does bump up against the widely held belief in education that learning is a
social endeavour that is best supported by more knowledgeable others. (p. 73)
Selwyn highlights two themes that emerged from the learner stories narrated above.
The first theme is that individuals are able to adequately regulate their learning
behaviours and actions, and the second theme is the level of social engagement and
interaction that occurs in a MOOC.
Stories one, two and three portrayed learners who demonstrated relatively high
levels of self-regulation during their engagement in the MOOC. All three were able
to shape their learning in order to reach their desired goals. The variation in their
engagement during the MOOC reflected how the course was situated within the
individual contexts and interests of each learner. These three learners were able to
employ a range of learning behaviours and to pursue different pathways, in order to
meet their different goals and outcomes. They had the skills necessary to actively and
very deliberately determine the nature of their engagement, aligning their behaviours
with their course goals and personal ambitions.
Some MOOC providers have recognised this need to provide variation in engage-
ment and have designed courses to crowdsource data in areas of contemporary social
interest. For example, three MOOCs from the University of Edinburgh (UK) used
this strategy. A MOOC on Behavioural Economics invited learners to participate in
an analysis of European dietary choices; a group of astrobiologists created an inter-
national community of people interested in research into life on other planets; and, in
2014 during the run-up to the Scottish Independence referendum, a group of political
science academics ran a number of opinion polls during the MOOC ‘Toward Scottish
Independence? Understanding the Referendum’. These opinion and data gathering
activities helped to sustain engagement throughout each MOOC. There were signs
of reduced engagement, although the rate of reducing activity within these MOOCs
over time was less striking than in many other MOOCs. Though it is difficult to link
sustained learner engagement with the MOOC activities, or their connection with
current affairs and events outside the MOOC.
Selwyn (2016) identified the absence of socialisation in much online learning.
MOOCs allow opportunities for massive numbers of learners to develop through
mutual forms of engagement. However, there is evidence that many MOOC learners
do most of their learning on their own (see, for example Littlejohn et al. 2016; Alario-
Hoyos et al. 2014). Yet learners’ behaviour may be similar whether the MOOC is run
as a live event (in-session, instructor-led with the opportunity to earn a certificate)
or as an archived course (standalone materials, self-directed course with minimal
instructional support and peer student presence, no deadlines, no peer-assessment,
and no opportunity to earn credit) (Campbell et al. 2014). Even when there are many
people learning at the same time, learners may choose to work on their own, rather
than taking the opportunity to learn with other people. One reason may be because
the course design offers few opportunities to interact with other people (Margaryan
et al. 2015).
MOOC learners find ways to organise themselves, finding ways to create oppor-
tunities for interaction. In some MOOCs students plan collaboration and interaction
via social media (e.g. Facebook, WhatsApp, etc.) or with colleagues, family and
friends (Lin et al. 2015). Other learners organise face-to-face meet-ups in locations
around the world (Lin et al. 2015; Vale and Littlejohn 2014).
Less-experienced learners may find it challenging to understand how to engage
in a MOOC (Milligan et al. 2013), particularly where there is no overall course
summary or well-defined structure to scaffold their learning (Kop et al. 2011). In the
learner stories above, learner four struggled to determine his own learning journey and
consequently used the predefined, linear course structure to scaffold his learning. In
cases where MOOCs lack a clear structure or predefined learning journey, community
and peer support become more important. Learners who are unable to chart their own
learning pathways may rely on others to help scaffold their learning. They might
follow other learners’ pathways and actions, or seek advice as to their next steps.
However, not all learners feel comfortable engaging socially or collaboratively in a
MOOC setting (Milligan and Littlejohn 2016). Therefore, the student experience is
likely to be different depending on each individual’s prior learning experience.
Research has found that learner discussions and interactions on a MOOC tend
to be characterised by decreasing participation over time (Jordan 2014). There is
evidence that some conversations are restricted because the students have limited
experience and knowledge to drive forward analysis of key concepts (Sinha et al.
2014). People sometimes post their own perceptions and anecdotal evidence, which
may lead to the development of surface, rather than deep, analysis and dialogue.
Generally, MOOC learners have limited opportunities for one-to-one dialogue with
people who have more expertise or with tutors, particularly when the ratio of tutors
to students is thousands to one. Yet it is this sort of engagement with an expert that
might help to sustain interaction.
Another characteristic of discussion forums is that people with similar interests
and knowledge may work together, giving rise to a phenomenon termed ‘homophily’.
On the one hand, learning with people of similar interests and ability can be beneficial
(Wegerif 1998). On the other hand, homophily can lead to a narrowing of knowledge
and ideas, which can lead to high levels of activity and engagement within a MOOC,
leading to narrow knowledge development (Sinha et al. 2014).
4.6 Making Sense of the Learner Stories 73
Gillani and Eynon (2014) examined tens of thousands of comments in MOOC

discussion forums across a range of MOOCs. Their findings indicated that learners
may participate in discussions without completing assignments (like learner two in
the narratives above). They further detected declining participation in the discussion
forum over time. Over time the discussion participants formed small groups, with
20% of the participants contributing to 90% of the overall discussion. The motivations
for participating in the discussion varied, depending on the course and the learner,
and ranged from seeking help to contributing ideas.
These types of interactions are indicative of critical peer-supported learning pro-
cesses. Where learners are not supported directly in a MOOC by tutors or experts,
peer support becomes more crucial. Peer learning is supported by a number of tech-
nologies, both within the course on the MOOC platform and outside the course
boundary, via learners’ self-selected digital tools, such as Facebook and Twitter, and
also in non-digital settings (Kellogg et al. 2014; Shen and Kuo 2015; Sinha et al.
2014).
Sentiment analysis of a student’s contributions to a social media site or forum is
being investigated by Rosé and colleagues to support deeper analysis of affective
factors influencing learning (see, for example Yang et al. 2014). Learners may learn
more effectively when they are happy or when they feel challenged, though these
characteristics are likely to be tightly bound to the learner, rather than being general
factors (Boekaerts 1993). There is a view that using data analytics to gather informa-
tion about learners’ characteristics and motivations can help to design more attractive
courses and promote engagement, which may lead to better retention, engagement
and learning (Rienties and Rivers 2014).
What makes these measurements difficult is that these characteristics and moti-
vations extend beyond the boundaries of the MOOC; a learner may elect to drop out
of a MOOC because of a competing priority in her life. This situation emphasises
first the importance of gathering a broad range of data that enables engagement with
learner stories and narratives to complement the use of data analytics, and second,
that data associated with learners are dynamic and change over time––a learner may
intent to complete a MOOC then change her mind.
The fourth learner story illustrated above highlights the less empowered and agen-
tic MOOC learner. Learners who tend towards the fourth learner story typically have
less experience in self-directing their own learning and in deliberately modifying
their learning behaviours and actions in order to learn in the ways that are most
relevant to them. This type of learner might benefit from engaging in regulatory
activities, such as planning what they will do in the MOOC, monitoring and control-
ling these activities, and self -reflecting and evaluating their own learning (Milligan
et al. 2012). However, this chapter has illustrated that, given the apparent inability
to fully understand the nature of learning occurring through quantitative measures
alone, and the complexity of gathering and analysing qualitative data, designing high
quality, responsive learning on MOOCs is highly challenging.
MOOCs need to accommodate learners with—at certain times—opposed inten-
tions, motivations and goals. The learners themselves come with very different learn-
ing approaches, prior experiences and confidence in managing and directing their
own learning. Learners further are seeking significant variety in levels of social inter-
action and engagement in a MOOC. Given this diversity, understanding what makes
a ‘good’ or ‘high quality’ MOOC is an incredibly challenging question to answer.
Chapter 5 attempts to unpack the complexities around notions of quality in MOOCs.
The diversity of learners engaging with MOOCs has been well documented. And
there is a growing body of research exploring the learning implications associated
with this diversity. What we have attempted to argue in this chapter is the need to
ensure that this diversity is understood in a holistic, contextually mediated way. This
requires a move beyond current limits of quantitative data and learning analytics.
Learning is a deeply personal, context-dependent (which of course includes a social
dimension) undertaking. In order to fully appreciate the diversity of learning and
learners in MOOCs, it is necessary to engage with the qualitative learning stories of
individual learners. While the quest to open up access to massive numbers of learners
is a noble task, the reality is that deep learning will only be successful when each
individual learner is supported to engage in the learning process. This centrality of
the individual learner in discussions about quality in MOOCs, will be explored in
greater detail in Chap. 5.
Acknowledgments The authors wish to thank Vicky Murphy of The Open University for comments
and for proofing this chapter.
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Chapter 5
Designing for Quality?
Abstract There are significant complexities in interpreting and measuring quality

in MOOCs. In this chapter, we examine experts’ perceptions of how to measure
quality in MOOCs, using empirical data we gathered through conversations with
MOOC specialists. In their experience, while data can be helpful in understanding
quality, the metrics measured are shaped by underpinning assumptions and biases.
In conventional education, it is assumed that the learner wants to follow a course
pathway and complete a course. However, this assumption may not be valid in a
MOOC. Quality data might not capture the underlying goals and intentions of MOOC
learners. Therefore, it is difficult to measure whether or not a learner has achieved his
or her goals. We stress the need to explore quality metrics from the learner’s point of
view and to encompass the variability in motivations, needs and backgrounds, which
shape conceptions of quality for individuals.
5.1 Contested Purpose, Uncertain Quality
Establishing what denotes quality in MOOCs is a challenging proposition. Quality

is not objective. It is a measure for a specific purpose. Purpose in education is a
contested construct, which shifts depending on context and the perspective of the
particular actor—governments, institutions, corporations, teachers and academics,
researchers and learners. In traditional, formal education there has tended to be some
degree of consensus between the actors involved as to the overarching purpose of a
particular course, or programme, or educational pathway. In MOOCs, this common
ground is much harder to identify and maintain.
Reflections and questions about quality are inextricably tied up in a series of
complex questions, including: How do MOOCs transform education, for institu-
tions involved in the creation and delivery of education, and for learners? What
are the outcomes that should be expected or demanded from MOOCs? Are these
outcomes consistent and static or should they vary by actor and context? How
should the competing expectations and demands of MOOCs be balanced? And who
should decide?

https://doi.org/10.1007/978-981-10-8893-3_5
80 5 Designing for Quality?
MOOCs tend to be positioned as outside of traditional educational provision. And

while undoubtedly pushing boundaries and calling into question existing paradigms
and approaches in education, in reality they remain very much embedded within the
existing power structures and control of the pre-eminent institutions and corporations.
Selwyn (2014, p. 3) explains this power imbalance as a hierarchy that exists between
‘those that “do” educational technology’ and ‘those who have educational technology
“done” to them’. A hierarchy that encompasses, at its worst, dichotomous positions
of producer and consumer; empowered and exploited.
While the early rhetoric positioning MOOCs as a panacea that would democratise
access and outcomes in education has diminished, there remains an overzealous
and frequently uncritical assumption that the new, and the digital must be good.
As Selwyn (2016) explains ‘the values and meanings that are attached to the idea
of digital education could be seen as just as significant as any actual use of digital
technology’ (p. 8). That is, there is minimal rigorous, empirical evidence that tech-
nology consistently leads either to improved teaching practices and opportunities or
to improved learning outcomes.
This chapter explores these claims and the problematic relationship between qual-
ity standards and educational innovation. Based on data gathered through our own
research, we explore the notions of quality in MOOCs.
5.2 Notions of Quality
The importance not just of education but of quality education is enshrined as Goal four
of the United Nation’s Transforming Our World: The 2030 Agenda for Sustainable
Development. It reads, ‘ensure inclusive and equitable quality education and promote
lifelong learning opportunities for all’. It goes on to explain in detail what this might
mean and the generic outcomes it will translate to for all people around the world.
However, while the broad outcomes are articulated, specific discussion of what a
quality education might look like and the inputs and processes that are required to
achieve the desired outcomes are notably absent. It is an absence that reflects the
inability to arrive at an absolute threshold standard of quality or a definitive list of
the specific criteria that quality education may be assessed against.
Gibbs (2010), in his report on quality in undergraduate education, utilised Biggs’s
(1993) 3P model of learning to explore notions and dimensions of quality. Biggs
conceptualises education as a complex set of interacting ecosystems, with a particular
programme or MOOC functioning as a single ecosystem. To understand how each
educational ecosystem operates, it is necessary to break it into its constituent parts,
and to explore the position and operation of each part both individually and in relation
to the other parts that come together form the whole. Moreover, it is also necessary
to understand how each educational ecosystem is positioned in relation to other
ecologies.
5.2 Notions of Quality 81
Quality, therefore, is positioned as a context-dependent construct. That is, to

understand quality of a MOOC (or any educational or learning opportunity and expe-
rience), it is necessary to situate the MOOC within the broader ecosystems in which
it operates. Quality, therefore, must encompass the changing educational, economic,
political, technological and social contexts (Hood and Littlejohn 2016a).
Biggs (1993) and Gibbs (2010) following him break any educational ecosystem
into three parts: presage, process and product variables, which relate broadly to
the input–environment–output model. Presage factors are the resources and factors
that go into an educational experience or product. In traditional learning, common
presage measures include funding and the allocation of funding into teaching, student
to staff ratios across institutions, the quality of teaching staff and the quality of
students entering an institution. Process variables refer to the processes and actions
associated with presage variables, including pedagogical models, instructional design
and learning materials. Product variables are the outputs or outcomes of educational
processes, which traditionally have been measured by student retention, completion
and certification, and grade levels.
These 3Ps apply to MOOCs. However, their particular composition and the mea-
sures that may be associated with them do not always transfer directly from more
traditional forms of education and learning. In many cases, the aspects of MOOCs
that led to early supporters labelling them as revolutionary and transformational
represent a redefinition of presage, process and product variables.
In early 2016, the authors undertook a survey of MOOC experts—people who
had experience in developing, researching and implementing MOOCs or MOOC
platforms from around the world (Hood and Littlejohn 2016b). The purpose of the
survey was to identify experts’ perceptions of how to measure quality in MOOCs.
To stimulate responses and to provoke an element of controversy or argumentation,
the authors provided four different scenarios for approaching quality in MOOCs.
Scenario one presented quality from the perspective of the learner, scenario two
in relation to pedagogical approach, scenario three from an instruction design per-
spective and scenario four from an outputs perspective. The response from experts
demonstrates the challenges in understanding what quality means in a MOOC and
trying to construct and action a quality framework that will adequately accommodate
the complexity and mutability of MOOCs.
Below are presented excerpts from the responses. The views expressed represent
many of the ideas that will be explored in greater detail throughout this chapter.
In these first two quotes from MOOC experts, there is recognition of the multiple
actors whose voice and perspectives may inform any consideration and judgment on
quality in MOOCs.
According to the ISO (International Organization for Standardization), quality is defined as a
set of products and services features that matches the client’s demands. Client is considered
anyone who uses the system. According to the American Society for Quality (2014) in
technical usage, quality can have two meanings: 1. The characteristics of a product or service
that rely on its ability to satisfy stated or implied needs. 2. A product or service free of
deficiencies. The totality of features and characteristics of a product or service that relies on
its ability to satisfy given needs. Besides the different approaches to the concept of quality, it
is consensual that quality is a subjective term for which each person has her own definition.
Implicit in this description of quality is an understanding that quality is mutable and

context-rich and that a person’s perception of quality will be dependent on their par-
ticular orientation towards a product (or learning opportunity). That is, the producer
of a MOOC, or the platform provider may have very different measures and under-
standings of quality to the learner. The variety of perceptions and desired outcomes
of the different actors and agents involved in MOOCs led to another respondent
arguing that:
QA on MOOCs cannot be standarised as for some every online course is a MOOC and
MOOCs are used for several different aims. As such the purpose for using a MOOC differ
largely. Even with one MOOC there is no uniformal aims between actors involved (insti-
tution, the teaching staff involved and the participants). MOOCs are designed for various
target groups, and even within ‘one target group’ the motivation and intention of MOOC
participants vary a lot. Even the intention of one participant is likely to change during the
MOOC (as it is non-formal learning). Consequently quality of MOOCs can only be measured
against the design. I.e. the persons involved in QA of MOOCs must focus on a clear design
principle at early start of the development of the MOOC with clear indicators at different
actor/stakeholder level. And check if QA processes are in place to measure these indicators
and to adjust the design of the MOOCs during next iterations. This kind of QA processes
are for some part uniform, and in this way counteract that quality is context and cultural
dependent.
Another respondent similarly argued the important role of design in understanding

and assessing quality in MOOCs, this time making an explicit connection between the
commercial motivations of MOOC providers and the need for high-quality design:
How to measure quality depends on the goal of the MOOC: if the goal is to reach a new target
group then outcome measures make sense. If the goal is to make (commercial) publicity for
the organizing institution then the quality of the design and the materials is a better indication.
The importance of design could also be connected to the primacy of the learner as
product (as opposed to the more traditional learning as product).
We recognised that not only will each learner have a different response to the same
course/learning environment, but could also have a different response from moment to
moment. … This considers the relationship between design (structure) and learner expe-
rience (agency), which gives an indication of the success of the MOOC (another question
is whether success equates to quality). Considering quality in terms of learner experience
is to think of quality as measure of a product. But quality could also be a measure of the
process, i.e. the pedagogy or the instructional design. If the learner perception is one of a high
quality learning experience, then by implication the pedagogy and instructional design are
appropriate to their expectation. If the learner hasn’t had a good experience, then it doesn’t
matter how many of the pedagogy or instructional design quality criteria boxes have been
ticked.
The learner is positioned as the central outcome measure and design pivotal in the
construction of the learner. The primacy of the learner in understandings of quality
5.2 Notions of Quality 83
was the dominant perspective of participants. There is recognition, in particular,

of the diversity of learners’ participation in MOOCs and the varying motivations,
learning dispositions and goals that they brought to a MOOC, as the three quotations
below indicate.
With such a diverse audience, it’s very difficult to define quality in any way which doesn’t
take learners’ experiences into account. Of course, this makes the measurement of MOOC
quality difficult—because what’s ‘quality’ for one learner may be wildly different to ‘quality’
for another learner. Platforms and course creators are still working out which proxies might
be best used to measure quality in this context. As a starting point, many have used traditional
outcome measures such as completion and certification. But this has likely led some providers
to misjudge where they are being successful and where they need to improve, erroneously
judging courses to be a success simply because their audiences are more comparable to
those of formal education. This approach risks wasting the potential of MOOCs to open up
education to less traditional types of learners and motivations, instead emphasising a focus
on more traditional experiences translated into a MOOC environment.
I’m not convinced we are able to achieve a stable measurement of quality when participants
are so varied in their intentions, backgrounds and experiences. For me the best approach
would include questions that aim to understand where participants are starting from (what
are you hoping/expecting to get out of this course) and where they end up, over a reasonable
period of time. It would also involve understanding what range of experiences the course has
been designed to make possible for participants. And it would leave plenty of room for the
unanticipated, and use inventive measures of engagement (like intensity of activity around
particular aspects of the course; prevalence of knowledge exchange beyond the course) to
try to expose surprising outcomes.
I’m biased towards learner empowerment, meaning that I’d base MOOC quality on the job
the learner wants the MOOC to do for them. For some it’s enjoyment and curiosity. For
others it’s gaining a credential that enables them to further their career. For others it could be
a mandatory requirement of a new position or framework. These are not mutually-exclusive.
The thing we need to be careful about is Campbell’s Law1 which warns us against putting
numbers against social indicators. This leads to pressure on increasing the number, no matter
what it is. The trouble is that “caring doesn’t scale, and scaling doesn’t care”. Which means
that while xMOOCs have, up to now, been all about numbers, we might need a way to build
communities. In other words, perhaps we need to go back to the original, more rhizomatic,
and community-focused vision of MOOCs.
The final quotation included here identifies the subjective nature of quality, and the
importance of questioning not just what but for whom. The questions this participant
raises will be explored in the remainder of this chapter.
The key seems to be the role in what measuring quality is for. It is not an objective mea-
sure—but a measure with a purpose—who would use it, why (consumer, producer or other)?
I suspect the entirely valid perspectives above relate for whom the quality question is
being asked (i.e. it is not without purpose and its purpose may focus the nature of the
qualities being measured and promoted). Consumer—may well want to know the supplier
is meeting certain quality thresholds (cognitive outcomes, engagement/fun, ease of use,
employability/promotion or other social capital outcomes [networks, credibility, ‘interest-
ingness’]) thresholds in deciding which MOOC (or MOOC platform) to choose. Produc-
er—with MOOC platform competition the supplier and or platform owner may want to earn
1 Campbell’s law—The more any quantitative social indicator is used for social decision-making,
the more subject it will be to corruption pressures and the more apt it will be to distort and corrupt
the social processes it is intended to monitor.
recognition amongst their consumers and or ‘Others’ for reaching quality thresholds. They
may just want to achieve business outcomes (brand impact, enquiry rates, income, disrup-
tion to/improvement of pedagogy value). This will depend (probably). This may of course
itself depend on what consumers most value (in the end what they are willing to pay for the
certificate—they may not care about pedagogic inventiveness if outcomes remain the same)
alongside the business (or social mission) interests of the Producer and needs of the 3rd party.
External 3rd Party—an employer/educational institute way wish to recognise ‘success’ in
the MOOC as recognition of the competency/capacity of the individual (cognitive, or attitu-
dinal). A government department may wish to be assured their HEIs are moving along the
e-engagement pathway. It seems one ‘true’ measure of quality is unlikely without knowing
the purpose. Quality for what and whom?
5.3 Quality of Platform Provider
The quality of the platform, and perhaps more importantly the structure and oper-
ation of the organisation that administers it, plays an important role in establishing
the access, reach and design of the MOOCs. It is essential to situate any discussion
of platform quality in relation to the main objectives and audience of the MOOC
platform, for example, the MOOC platform provider Coursera is focused on profes-
sional training, while FutureLearn is centred on dialogic processes for more generic
learning applications.
MOOC platforms have reoriented their models since their inception in 2011. Early
offerings tended towards traditional university courses, organised into short chunks of
learning (ten to twelve week blocks) with fixed start and end dates. Content delivery
and design focused on videoed lecture content, weekly or biweekly assignments and
a final exam. These early MOOCs tended to be open for access by anyone and free
of charge.
Since then MOOCs have been providing increasingly flexible offerings. In par-
ticular, there has been a noticeable shift towards the more lucrative employment and
professional learning or upskilling market. Some MOOCs are no longer open or free
and are designed to provide professional development for specific employers, includ-
ing government organisations (for example the UK tax department, Her Majesty’s
Revenue and Customs, HMRC), companies (for example Wellcome) and organisa-
tions such as the British Council (see the British Council MOOCs on FutureLearn at
https://www.futurelearn.com/partners/british-council). MOOCs are responding both
to learner behaviour and to commercial propositions, both of which pose questions
for notions of quality.
MOOC platforms have been searching for sustainable business models. This has
led to MOOC providers launching paid-for content or participation. Usually asso-
ciated with some form of credentialing or accreditation at the end. Degree courses,
such as the Computer Science Masters offered as a collaboration between Georgia
Tech and Udacity, the iMBA created by Coursera and the University of Illinois, and
Udacity’s Nanodegrees, have also been developed for learners who specifically want
to engage in a more formal, structured course of study. Udacity, in particular has
5.3 Quality of Platform Provider 85
partnered with corporations to provide employment-facing learning opportunities,

such as the nanodegree in self-driving cars that use instructors from Mercedes-Benz
and Nvidia, or the course on Android operating systems developed in conjunction
with Google. Students pay $199–299 per month for these MOOCs. Udacity backs
the quality of their offers by a premium version of its nanodegree, which for an
extra $100 per month provides a money-back guarantee to graduates if they do not
get a job within 6 months of completion. Coursera and edX both offer fully pay-for
courses and the UK-based FutureLearn is launching a paid-for course as part of six
postgraduate degrees from Deakin University.
When the focus shifts from opening access to education, and particularly presti-
gious institutions of higher learning, to the business models and commercialisation
of this access and the learning that entails, conceptions of quality are altered. Quality
is no longer focused on the learning, per se, but on how the learning can facilitate a
consistent and profitable revenue stream. The business case for a MOOC becomes
more important than the social good a MOOC could provide. And it has become
evident that lifelong learners are no longer the primary target of MOOCs. While
there are still opportunities to audit courses for free, the real money, and therefore
the primary focus for MOOC providers, is in the professional development courses.
MOOC platform providers also are responding to what they are learning about
student motivations and typical learner behaviours in MOOCs. In particular, they
are recognising the greater need for flexibility in ways in which they structure and
offer shorter courses. Among other things, this has led to shorter courses, which
often are unbounded, that is with flexible start and end dates and softer deadlines for
assignments. To respond to learner dropout rates, courses have been reduced from the
original 10 to 12 weeks to 6 weeks or even micro MOOCs lasting hours. Similarly,
content is structured to fit with learners’ behaviour. For example, videos tend to be
limited to a maximum of 6 minutes, as a majority of learners will not watch beyond
this. Research has further found that including in-video quizzes or instructor slides
(Guo et al. 2014; Mamgain et al. 2014) and the inclusion of subtitles and the ability
to vary the video speed (Mamgain et al. 2014) have been found to increase learners’
perceptions of video content.
On the surface, this responsiveness to learner behaviour and implied, if not articu-
lated demands, is a positive step. However, it has potential unintended consequences.
Decisions regarding instructional design and pedagogy are not necessarily under-
pinned by what is known about how people learn, or how to structure learning to
support the learning process. For instance, the flexibility of course start and end
dates, while allowing for greater accessibility for learners, has also led to decreasing
activity on discussion forum and less peer-to-peer interaction. Flexibility impacts
learner success due to a number of factors. First, the link between learners’ partici-
pation in discussion forums and completion (Gillani and Eynon 2014; Kizilcec et al.
2013; Sinha et al. 2014). Second, the role that peer interactions play in supporting
learning and knowledge building activities (Amo 2013; Conole 2013; Hew 2014;
Margaryan et al. 2015). Third, the oppotunites for help-seeking and peer assistance
that flexibility in course start and end dates facilitate (Amo 2013; Guardia et al. 2013;
Hew 2014).
MOOCs also are tending to be divided into smaller chunks, focusing on more
discrete areas of content or specific skills. As Shah (2016) identifies, some older
courses have been split into credentials. For example, the MOOC on Probabilistic
Graphical Models (developed by Coursera’s co-founder Daphne Koller) has now
been split into three courses and is a Coursera Specialisation in its own right. These
specialisations are course designs that link several smaller MOOCs to form a larger,
coherent programme of learning, although it is possible to enrol only in a single course
and not undertake the whole specialisation. This breaking up of content can be linked
to a quest for greater revenue and the need for MOOC platforms to make money, as
discussed in general in Chap. 1. It also highlights another trend in education which
is the fragmentation of content and curricula. The construction of MOOCs, and their
push towards providing employment-focused skills and courses might potentially
exacerbate what Cleveland (1985) describes as:
It is a well-known scandal that our whole educational system is geared more to categorizing
and analyzing patches of knowledge than to threading them together. (p. 20)
5.4 Quality of Instructor
The role of the instructor in a MOOC has important implications for the learning
that occurs. However, the roles adopted by the instructor and the impact that they
have vary substantially between MOOCs. To date, the three most common types of
instructors in MOOCs are: (1) the distant rock star or academic celebrity lecturer; (2)
the co-participant or facilitator within a network and (3) the automated processes that
act as a proxy to human tutor or assessor (Bayne and Ross 2014; Rodriguez 2012). A
further role has emerged recently, that of the pay-for personal mentor, who provides
1:1 feedback, email and forum support and live weekly office hours (Morrison 2014).
Radically new ways to connect with instructors are emerging. Learners connect to a
central hub using a mobile app which then connects them with a tutor or other forms
of help (e.g. experts or peers) from around the world. A tracking system enables fees
to be charged and transferred from the student to the tutor or organisation. Online
assessments verify the competence and skills of the learner and their identity and a
blockchain system records each transaction, so that the student has a verified set of
qualification associated with him or her (Sharples and Domingue 2016).
Similar to traditional, offline courses, the instructor in a MOOC determines or
mediates the pedagogical approaches that are employed, the level of teaching skill
and familiarity with content, and the opportunities for instructor–learner interaction
and engagement during the course period. Designing and running a MOOC is a
labour-intensive activity. Kolowich (2013) determined that a MOOC typically takes
over 100 h of precourse set-up time and then an additional 10 h per week during
the running of the course. However, many educators are not recognised for the work
they put into designing and running a MOOC, in the way their ‘traditional’ duties are
credited. Ross et al. (2014) argue for the importance of acknowledging the complexity
5.4 Quality of Instructor 87
of teacher positions and experiences in MOOCs and how these influence learner
engagement. Data suggest that the instructor has a significant impact on learner
retention in MOOCs (Adamopoulos 2013). Further research suggests that instructors’
participation in discussion forum activity and actively supporting learners during the
running of a MOOC positively influences learning outcomes (Coetzee et al. 2015;
Deslauriers et al. 2011).
5.5 Quality of Learning Design
Illeris (2007) suggests that the learner’s abilities, insight and understanding are
developed through the content dimension of the learning experience. That is, what
the learner can do, knows and understands. Examination of content in MOOCs (or
arguably any learning experience) cannot be separated from the instructional design
and pedagogical frameworks within which it is situated. There is an inherent tension
in MOOCs between the product and process elements of their design.
The flexibility of participation and the self-directed nature of engagement, which
enable learners to self-select the learning opportunities and pathways they fol-
low when participating in a MOOC (DeBoer et al. 2014), necessitate the re-
operationalisation of many of the process variables typically involved in education.
The potential massive number of learners and the diversity of participants has signif-
icant implications for the learning systems and pedagogical approaches required to
support these learners. Downes (2013) suggests that this involves the consideration of
how to circulate content effectively and to support meaningful interactions between
learners. Tyler (1939) contends that content delivery cannot exist in isolation; the
value of content is related only to the use and interpretation of the content in specific
contexts. Tyler’s view highlights the challenge involved in MOOC design, given the
multiple, diverse contexts of individual learners.
Questions emerge regarding the balance between structure (intended to provide
direction) and self-regulation, between breadth and depth of content, and whether to
emphasise instruction or self-directed learning. Further questions exist around the
employment of broadcast or dialogue models of delivery, whether MOOCs should
offer edutainment or deep learning opportunities, and whether and how to promote
homophily or diversity in learners’ engagement and participation.
Research has explored how the nature and presentation of content in MOOCs
influences learners’ perceptions of the learning experience. Perceived richness of the
course content has been found to be correlated positively with learners’ perceptions
of their knowledge comprehension and the quality of the learning exchanges that
occur (Lin et al. 2015), as well as successful completion of a course (Adamopoulous
2013). The use and creation of high-quality, authentic resources and content (Amo
2013; Conole 2013; Margaryan et al. 2015), which are connected to practical, real-
life examples (Grunewald et al. 2013; Littlejohn et al. 2016) and the opportunities
for quality knowledge creation throughout the course of the MOOC (Guardia et al.
2013) are also associated with effective instructional design.
While content quality is central to learners’ perceptions of the MOOC experience,

Dillenbourg et al. (2014) warn that there is a danger in MOOCs to focus too heavily on
the engagement of learners and the professionalism of the preparation and execution
of content at the expense of learning effectiveness and the fulfilment of learning
objectives. Here the concept of MOOC and learning as edutainment, and the learner
as consumer, are in potential conflict with the integrity and richness of the learning
experience.
Content without a corresponding focus on instructional design and pedagogical
framing impacts the acquisition process, see Chap. 4 for a discussion of the acqui-
sition in the learning process. Instructional design in MOOCs is complicated by the
need to adequately accommodate the diversity of the learner population and the need
to provide learning activities that cater to and support different learning styles and
needs (Alario-Hoyos et al. 2014; Guardia et al. 2013; Hew 2014; Margaryan et al.
2015), while also adhering to a coherent overarching design, which incorporates
support structures to scaffold the learning journey. Research suggests that effective
instructional design (both in MOOCs and more broadly in any learning experience)
should empower learners (Amo 2013; Guardia et al. 2013), offering opportunities for
personalised learning (Istrate and Kestens 2015) which drawing on learners’ individ-
ual contexts and previous experiences (Scagnoli 2012). Integral to this is a consistent
vision, which provides a clear and coherent framework in which to embed the content
and pedagogical approaches (Conole 2013; Istrate and Kestens 2015; Warburton and
Mor 2015). The consistent vision must also facilitate a degree of autonomy and the
presence of differentiated pathways and flexibility has been connected to learners’
perseverance in a MOOC (Jordan 2015; Perna et al. 2014).
Appropriate use of digital technology tools is important to the design and delivery
of high-quality learning experiences and opportunities (Amo 2013; Conole 2013;
Guardia et al. 2013; Istrate and Kesten 2015). There is considerable opportunity
to utilise learning analytics to better personalise and tailor MOOCs to learners
(Daradoumis et al. 2013; Kanwar 2013; Lackner et al. 2015; Sinha et al. 2014;
Tabba and Medouri 2013). Chandrasekaran et al. (2015) have called for automated
methods to aid instructors in responding to student feedback and questions, while
Kay et al. (2013) suggested that learning analytics can be used to better understand
knowledge creation and learning processes in MOOCs. However, as outlined in
Chap. 4, the application of data analytics to MOOC learning processes is complex
yet often oversimplified.
5.6 Quality of Adaptability to Context
Learners and learning in MOOCs are situated within multiple contexts, spanning
both online and offline dimensions. To fully understand the role and positions of
these contexts, it is necessary to consider not only how the human actors or learners
engage with and through them, but also how the role of nonhuman materials and
entities enter, engage in and shape the spaces. The social shaping of technology and
5.6 Quality of Adaptability to Context 89
physical objects through language, practice and interactions (sometimes called socio-
materiality) provide useful lenses for examining the interdependencies of MOOCs
and their contexts. Kling and Courtright (2003, p. 223) position online sites from a
sociotechnical perspective as being:
… structured sociotechnically, co-configured not only by the constraints and affordances of
the technologies involved but also—and primarily—by social, economic, and institutional
factors.
This is similar to Fenwick’s (2015) socio-materiality perspective, in which she sug-

gests that:
What socio-material approaches offer to educational research are resources to systematically
consider both the patterns as well as the unpredictability that makes educational activity
possible. They promote methods by which to recognise and trace the multifarious strug-
gles, negotiations and accommodations whose effects constitute the ‘things’ in education:
students, teachers, learning activities and spaces, knowledge representations such as texts,
pedagogy, curriculum content, and so forth. (p. 84)
These perspectives provide a useful means for engaging with the intricate relation-
ships that emerge between learners, technology, content and environments, with each
actor actively shaping and influencing the patterns of behaviour and structuring the
learning that arises.
The socialisation between individuals and groups of individuals and how this
socialisation process shapes and is shaped by the technological infrastructure is a
much-discussed element of learning in MOOCs. This active interweaving of different
actors and materials is described by Fenwick (2015):
Everything is performed into existence in webs of relations. Materials are enacted, not
inert; they are matter and they matter. They act, together with other types of things and
forces, to exclude, invite, and regulate activity. This is not arguing that objects have agency:
an essay does not write itself. But its particular production is an agentic assemblage of
assignment protocols and literary traditions, books and other content sources (entailing all the
materialities of library lineups, slow internet browsers, fortuitous tweets etc.), post-it notes
and piles of paper and iPads, the particular affordances and directives of word processing
software—all working in and through human bodies and consciousness. (p. 87)
Consequently, any discussion of quality in MOOCs must consider the connections

and interdependencies that are facilitated by the technological infrastructure to enable
each individual learner to construct their network of learning tools and resources to
support them in achieving their goals. For some learners, the network of tools and
resources provided on the MOOC become their primary network of learning mate-
rials. However, interview data from participants of a ‘Introduction to Data Science’
MOOC offered by the University of Washington through Coursera indicate the broad
range of materials they engage with.
One participant explained how he worked through a difficult assessment task,
describing the rich interactions with people and resources that constitute his learning:
I tried for a whole day, not exactly a whole day, let’s say a couple of hours, but I mean I was
in the office before that. I had my good friend here, my previous colleague, so we used to
study together anyway, so I generally end up discussing with him first … I actually try to
solve the problems mostly myself or I generally have a close group of friends we generally
discuss things and that is how I have been learning so far … so I end up getting resources
from there if I need something.
Another participant described a different approach. Rather than interacting with

people, he preferred to draw upon his network of learning of resources:
When I need to learn something I will usually try to do it myself, usually with the help of
Google and textbooks rather than to seek out another person, other human being or to seek out
a formal training opportunity. Yeah so it comes down to my imposter syndrome, I don’t like
admitting that I don’t know things … if I run into a problem, usually in debugging something,
my first port of call is to Google, you know dictionaries in Python say or something like that
and that will usually throw up some stack overflow answers or some various random blog
posts or the Python documentation or W3 schools or all of the stuff.
These quotes illustrate the diverse ways MOOC participants learn. It is clear that it
is difficult to define a set of behaviours that can be modelled or used accurately to
predict the outcomes of learning relative to the learner’s goals.
However, technology and learning analytic systems have the potential to play
a considerable role in iteratively shaping the learning experience on MOOCs by
offering insight into learner and instructor behaviour and activity. For example, new
technologies and techniques are being developed that facilitate the automated analysis
of discussion in MOOCs. These include technology for analysing discussions for
learning (Howley et al. 2013), the formation of discussion groups (Yang et al. 2014)
and indicators of motivation, cognitive engagement and attitudes towards the course
(Wen et al. 2014a, b).
5.7 Quality of Outcome
In traditional models of higher education, the most common measure used to indicate
the quality of the product is the proportion of students gaining a degree, and the
level at which the degree is gained (Gibbs 2010). The extents to which graduating
students gain employment in a field relevant to their degree and their starting salary
level are other common dimensions of quality (Gibbs 2010). The MOOC literature
frequently has employed retention, completion and certification rates as measures
of quality. However, given the range of motivations and goals that learners bring to
their participation in MOOCs, the product in a MOOC is not standardised across all
learners.
Grover et al. (2013, p. 1) suggest the question ‘What makes a good MOOC?’
needs to be reframed as ‘How can we make a MOOC work for as many of its diverse
participants as possible?’. Some educationalists have suggested a solution is to ensure
the MOOC design is optimised for the maximum number of students. However, rather
than emancipating the MOOC learner and enabling his or her to follow a personalised
pathway, this stance forces learner to comply with an ‘optimal’ design.
Enabling MOOCs to work for as many diverse learners as possible requires adapt-
ing the course for the learner, not the learner for the course. This involves reconcep-
5.7 Quality of Outcome 91
tualising participation and achievement according to the diverse motivations, goal

orientations and actions of participants (DeBoer et al. 2014). This, however, would
not be easy to measure. As Biesta (2007) argues, we have a history in education of
only measuring what can be easily measured, rather than that which cannot be readily
measured but nonetheless may be of great importance. Biesta further explains that
end up valuing whatever we can measure, whether or not it is of value to us. Morozov
(2014) explains that in MOOCs, learning analytics tend to be concerned with pre-
dictive and anticipatory action, with little consideration for questions of causation or
the context of consequences.
The new context of learning in MOOCs requires new measures of success and
quality to capture the diversity in participant behaviours and intentions (Bayne and
Ross 2014). This is a complex undertaking. It entails developing a ‘nuanced, strategic,
dynamic and contextual’ understanding of individual learners and individual MOOCs
(Mak et al. 2010, p. 280). It also fundamentally requires the reconceptualisation of
what MOOCs can bring to education.
This chapter has identified the need to develop new measures of quality in MOOCs
that take into consideration the diverse patterns of participation, which are influenced
by the individual motivations and goals of learners as well as their contexts, and the
subsequent range of outcomes in MOOCs. There is a need to focus quality mea-
sures more strongly around individual learners and to recognise the differentiated
product variables that MOOCs enable. This push towards interpreting quality out-
come measures in relation to individualised learning and individual learner outcomes
represents a significant break from traditional measures of product variables. When
discussing and assessing quality in MOOCs, it is necessary to situate the MOOC, the
learning opportunities it provides and individual learners within the multiple ecosys-
tems in which they interact. This new focus on the learner requires new thought
and the construction of reliable measures of confidence, experience and motivation,
which extend beyond self-report, could provide a more accurate view of quality than
conventional learner metrics.
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Chapter 6
A Crisis of Identity? Contradictions
and New Opportunities
Abstract Drawing on the previous chapters, this chapter explores four tensions that
characterise MOOCs. Although MOOCs are seen as an attempt to democratise edu-
cation, they often privilege the elite, rather than acting as an equaliser. MOOCS are
also considered a way to radically open access to education, yet they tend to offer
education to people who are already able to learn rather than providing opportunities
for everyone. While MOOCs are positioned as a disrupting force, often they replicate
the customs and values associated with formal education, rather than unsettling edu-
cational norms. MOOCs are conceived as social networks that allow learners to learn
through dialogue with others, yet many learners have limited interactions with others.
Even when learners have the ability to learn autonomously, they often are expected
to conform to course rules, rather than deciding their own learning strategies. These
problems may be accentuated where MOOCs are viewed as a set of products (content
and credentials) on sale to student consumers, rather than as a transformational edu-
cational experience for learners. The view of MOOCs as a product for the consumer
learner may overly simplify the complex, transformational processes that underscore
learning. Particularly where underlying automated systems try to improve progres-
sion by quantifying learners’ behaviours and ‘correcting’ these to fit an ‘ideal’ learner
profile or where algorithms and metrics are based on convectional education, rather
than on future-facing forms of learning. This chapter examines these problems with
MOOCs, offering promising future directions.
6.1 When Actions Contradict Aims
This book has exposed a number of inconsistencies that characterise MOOCs.

These courses are viewed by educationalists as a form of democratisation and in
Chap. 2 we examined whether and how MOOCs democratise the education land-
scape. Democracy is a levelling force that encourages equality. So it seems puzzling
that, by foregrounding the norms and power structures of pre-eminent institutions and
corporations, MOOCs might emphasise, rather than diminish, inequality. MOOCs
are also considered a disruptive force, with the potential to challenge existing educa-
tion models. Paradoxically, MOOCs sometimes reinforce conventions by requiring
https://doi.org/10.1007/978-981-10-8893-3_6
96 6 A Crisis of Identity? Contradictions …
learners to conform to accepted ‘ways of being’, a phenomenon which was explored

in Chap. 3. In Chap. 4, we interrogated how MOOCs accommodate massive numbers
of learners and discovered that many learners learn on their own. We concluded that,
rather than opening up education to everyone, MOOCs tend to create opportunities
for people who are already able to learn. Chapter 5 signalled a need to rethink the
metrics and measures that signal success. Retaining conventional metrics and mea-
sures may inadvertently create a new order between those who have control of course
designs and data and learners, particularly where course designs are linked to data
and analytics-based decisions. More worryingly, learners may be being exploited
to achieve the economic and performance outcomes preferred by the providers of
MOOCs, rather than being supported to achieve their own ideal outcomes.
These inconsistencies are apparent in other forms of open, online education, not
only MOOCs, so the issues highlighted in this book likely affect many different
areas of online education and lifelong learning. In this chapter, we further examine
these issues, in relation to their broader social, political and economic contexts, to
identify ways forward both for MOOCs and online education more generally. We
focus specifically on the promise of MOOCs as a democratising force and as a
means to disrupt and reorientate education. The success of MOOCs and future open,
online learning is linked to the ability of learners to learn. Thus, we emphasise the
importance of focusing attention on preparing learners to learn in a freeform manner
in open and unstructured environments, over designing courses to support masses of
learners to follow course pathways.
6.2 Restraining Elitism, Embracing Democracy
An asset of MOOCs that is underutilised is their unbounded geographical locations.

Moving away from the idea of a geographically located institution that offers courses
in a single, physical location means that learners and academics no longer have to be
scholars in a single institution, allowing them to work across numerous academies
and sites. These changes could disrupt the system of networking and cronyism that
originated in social class systems and has pervaded the elite universities for centuries,
maximising return for the members of these institutions. And indeed there have been
examples of MOOCs breaking open the stronghold of elite institutions, either by
identifying exceptional students who otherwise would not have applied to attend the
universities or by offering for-credit courses or degree programmes. However, these
continue to be the exception rather than the rule.
More commonly, rather than using MOOCs as a way to equalise, they are viewed
as a way to offer organisations a global perspective. In the previous chapters, we
illustrated how MOOCs can be used as networks of communication and control to
strengthen and solidify the dominance of pre-eminent universities over larger and
wider groups of people globally. MOOC platforms, with their non-geospatial loca-
tion, allow universities and organisations to rescale their authority from the level of
the institution to the level of ‘the global’. MOOCs are being used in ways that support
6.2 Restraining Elitism, Embracing Democracy 97
universities to build transnational identities that affords greater global reach, rein-
forcing their worldwide dominance. In this way, MOOCs amplify divisions between
elite institutions and organisations other education providers, rather than filling the
gaps. This expansion of ‘global brands’ feeds the corporate interests of the organisa-
tions that provide MOOCs—universities, industry and MOOC platform providers.
However, there are ways to restrain elitism and provide democratic solutions.
There is a drive from Governments and Non-Governmental Organisations (NGOs)
worldwide to focus on inclusion agendas, with a commitment to ‘make all voices
count’ and ‘leave no one behind’. This agenda is important for civil society effec-
tiveness, particularly for building capacity in countries where diversity is increasing.
While diversity is increasing in the US, Canada, Australia, New Zealand and the
European Union, there is also migration to and within Africa, parts of Asia and
South America. So, there is a need for globally responsive, democratic education
spaces that bring people together in informal and relatively unstructured networks to
engage critically with concepts, and work collectively to generate new knowledge.
Democratic spaces are important for groups of learners who are under-represented
or undervalued by society. For example, migrants reorienting themselves in a new
place of residence, minority groups seeking to advance their views or specialist
communities who want to exchange and share their knowledge. The work of NGOs
in supporting learning for these groups offers a blueprint for ways in which MOOCs
could become democratic.
One example is Kiron, a non-governmental organisation based in Germany that
works with refugees to help them learn how to live and work in the country. Kiron
uses MOOCs as a platform from which to allow refugees to begin their study in their
new country of residence, as illustrated in the case example.
Case Example: Supporting refugees’ learning

Refugees need support in facing the challenges of fleeing from their home
countries and starting over elsewhere. Yet, they have limited opportunities to
begin or continue their studies or even to learn about the new culture and
context where they are living. Kiron is an NGO that works with partner uni-
versities to offer MOOCs to refugees in camps in Germany (www.kiron.ngo).
They use a combination of MOOC courses, online collaboration platforms
and in-person learner support to help refugee learners. Each learner selects
a cluster of MOOCs bundled into modules that form coherent educational
programmes. Kiron negotiates recognition of prior learning with the partner
universities, who can award up to 60 credits for completed Kiron modules
using the European Credit Transfer and Accumulation System (ECTS). The
MOOC-based study means that refugees can continue to learn even if they
have to move geographically. After 2 years, Kiron students can apply to a
partner university to complete the third and fourth year of study for a Bache-
lor’s degree.
The case example from Kiron illustrates one-way MOOCs can be used as an
equaliser to ease transition. Learning at a distance is helpful for people who are
moving from one geographic location to another and the in-person learner support
helps refugees not only to learn the academic subject but to orientate themselves
in their new place of residence, supporting their development and helping them to
become productive and participate equally within society.
In countries such as India, where the higher education system needs to be expanded
rapidly, expansion of education largely is through private providers that tend to be
confined to narrow professional tracks and are regulated through weak internal and
state governance. In 2013, almost 90% of Indian colleges were rated as below average
on quality parameters. MOOCs are viewed as a way to alleviate some of India’s access
and quality issues in higher education by enabling larger groups of people to have
access to high quality learning. This expansion of education is particularly important
for under-represented groups within Indian society. However, most MOOC partici-
pants in India are already well educated and live within the urban areas, reflecting
learner trends from around the world. Expanding access requires MOOC providers
to understand the needs of people in poorer, rural areas who have limited access to
the internet and to technology devices that allow them to learn online. US-based
MOOC provider edX has formed partnerships with Indian Institutions, including the
Indian Institute of Technology in Bombay, to help them understand how they can
provide MOOCs for under-represented groups in India. The British Council and the
Open University is also working with Indian University Vice Chancellors to find
solutions to expanding education in India. More examples like these of the use of
MOOCs to equalise participation in society would help build the case for MOOCs
as a democratising force worldwide, rather than as a form of control.
6.3 MOOCs as a Disrupting, not Reinforcing, Influence
MOOCs are configured to subvert the conventional social order of education

(Siemens et al. 2010; Downes 2011). Yet, in some ways, they reinforce traditional
patterns and behaviours in education. This effect is apparent from the earliest Con-
nectivist MOOCs (cMOOCs) described in the previous chapters. The degree to
which cMOOCs disrupt education, particularly their openness to different modes
of behaviour, can be contested. They do not always allow for learner autonomy, as
there is an expectation, by the MOOC facilitators and by some of the participants,
that learners will adhere to prescribed ‘norms’ of behaviour. This issue is illustrated
through a study of self-regulated learning in the Change11 MOOC (Milligan et al.
2013).
Change11 was a MOOC that took place over 35 weeks, from September 2011 to
May 2012, with more than 2300 participants. The MOOC environment comprised
an informal network with a variety of loosely connected digital platforms and tools
including a registration portal, weekly online seminars and a range of blogs, tweets,
videos and other materials from the instructors. A newsletter emailed daily to every
6.3 MOOCs as a Disrupting, not Reinforcing, Influence 99
registered participant included course announcements, links to blog posts and tweets
from the participants. A link to any social media post from a participant using the
hashtag#change11 was included in the newsletter.
There were three types of participation in the MOOC: active, passive and invisible.
Active participants created and shared knowledge as blogs, tweets or comments on
other’s postings, created as original thought pieces or as spontaneous responses to
other people’s ideas. One active participant described his engagement, commenting,
“I have no idea how scattered I am across this MOOC, I have no idea how many
contributions I’ve made, 30? 50? I’ve got a lot of replies … I usually end a reply on
an open end [to encourage a response]” (P05).
A ‘passive’ participant explained her reservations about engaging in the MOOC:
“Sure, I can read other people’s blogs and that’s not a problem and I comment
occasionally, but as far as really putting my ideas out there in the open in my own
blog to be trampled on, you know there’s a bit of fear there I think that I have and so
that has been difficult for me” (P12). This reticence led to her being less visible to
other participants. From a learning analytics perspective, she may have seemed less
engaged than other participants. However, in her view, she was learning.
Invisible learners included participants who chose to drop in and out of the MOOC,
observing what was happening within the network but not contributing directly. One
participant described this behaviour as “hugely beneficial. Knowledge is filtered by
the course organisers and has more value than something I randomly come across
on the Internet” (P18). Some who were inactive within the Change11 network were
discussing the course with other people offline, or engaging in ‘closed’ social media
groups, on Facebook or other platforms. They learnt within small, circumspect groups
instead of openly contributing ideas to the network. Change11 participants who were
openly and actively contributing ideas to the network were frustrated with these
seemingly inactive members. Nevertheless, both groups—those who openly posted
ideas and those who worked in smaller, closed groups—were learning in ways that
suited their personal needs.
At one level, the contribution of knowledge by different people is based on a
democratic assemblage, where educational hierarchy is replaced by a flatter, more
horizontal structure. However, there are concerns that active participants are being
deprived of the insights from the invisible participants. Do all participants have a
duty to contribute to the dialogue in a MOOC in ways that allow others to learn from
their experiences? Is there a responsibility for every MOOC learner to be, at the same
time, a MOOC teacher. For MOOCs to become democratic spaces should learners
have the freedom to participate in a MOOC in the ways that are meaningful to them,
rather than in ways stipulated by the tutors?
Ideally, everyone in the MOOC would have the confidence and ability to be able
to put forward and test their own ideas and understanding. For passive participants,
an inability to contribute knowledge could be considered a form of illiteracy that
diminishes the democratic power of a MOOC. By never contributing, these partici-
pants are also not learning how to overcome that illiteracy. It could be argued that, to
enable MOOCs as democratic spaces, effort should be put into ensuring everyone has
the ability to contribute visibly. Equally, it could be contended that, in a democracy,
everyone should be able to participate as they choose. And there is ample evidence
to suggest passive participants are learning and gaining benefits.
Downes (2011) identified four important characteristics of cMOOCs—autonomy,
diversity, openness and interactivity. However, autonomy and diversity in partic-
ipation lead to tensions within the MOOC. Ideally in a cMOOC each learner is
expected to contribute to the learning of other people through interactions and collec-
tive knowledge building activities. However, this expectation prevents some learners
from autonomously learning outside the MOOC (Mackness et al. 2010). There is an
expectation by the MOOC designers and some of the learners that participants will
conform to the tacit ‘norms’ of the MOOC by behaving as visible and active partic-
ipants. Thus, although notionally participant can learn autonomously in a MOOC,
tensions may arise when learners use different forms of participation. In this way,
MOOCs reinforce some of the norms of education.
The previous chapters delineated the considerable potential of MOOCs to disrupt
education. However, MOOC innovations are being stifled in some ways by the culture
and values that pervade education, such that MOOC innovations appear to be at the
margins of formal education. However, these cultural values and norms are less
apparent where MOOCs are used to support professional learning, or learning for
work.
Professional learning is important in a world characterised by new forms labour
(Billett 2004). Hardt and Negri (2009) describe this transformation as a shift from
‘material labour’, where manufactured products are created by a stable workforce,
to ‘immaterial labour’, where the provision of new services and knowledge super-
sedes the production of material goods. Consequently, workplaces in many countries
have moved from being structured around production models, to being characterised
by flow of people, information and knowledge, which are fast, dynamic and dis-
orderly. Information and knowledge is now available as digital resources, used as
mediating artefacts or ‘social objects’ to connect people as they work (Engeström
2005; Knorr-Cetina 2001). It is the social interactions around MOOC resources that
form a basis for new teaching models (Ferguson and Sharples 2014), rather than the
availability of the MOOC itself.
Professional learning has been a growth area for MOOCs. Scenarios where MOOC
learning is integrated within work practice, and where people learn through social,
online interactions around their work activities, rather than in a standalone course,
provide a learning model that is disrupting professional training. Coursera has been
one of the first movers in this area, closely followed by edX and FutureLearn. There
are also examples of courses for professionals (or people training to become a pro-
fessional) that were offered independent of the mainstream MOOC platforms. These
include the Midwifery MOOC described in the case example below.
6.3 MOOCs as a Disrupting, not Reinforcing, Influence 101
Case Example: Integrating MOOC learning and work

The Evidence-Based Midwifery Practice MOOC aimed to support mid-
wives, midwifery educators and other health professionals in clinical
practice to develop knowledge of evidence-based practice (http://www.
moocformidwives.com/). The course was designed and facilitated by profes-
sional midwives from the University of Aalborg in Denmark and the University
of Technology Sydney in Australia. The MOOC ran over a 6-week period in
April and May 2015 and attracted 2098 students from countries in Europe,
Asia, America, Africa and Australasia. It was comprised of six modules popu-
lated with a range of learning resources, including video lectures and scientific
articles (Dalsgaard and Littlejohn, in press). Regular, synchronous, online pre-
sentations were offered, and participants were expected to interact and share
knowledge on midwifery practice in their geographic location through online,
text-based forum discussions.
The MOOC created opportunities for professionals to integrate their work and
learning. Each participant had to explain customary midwifery practices in their
own country. They shared their viewpoints on distinctive forms of practice, and the
likely consequences in different regional settings. Sharing practice examples was a
good first step towards changing and improving practice. The MOOC is an example
of a community of networked expertise identified by Hakkarainen et al. (2004), where
professional learning is based around social interactions within a network.
In previous chapters, we described how access to resources alone is not sufficient
for learning and expertise development, since learning requires active agency of the
learner. Even the most promising structured online resources do not encapsulate the
knowledge needed to support learning and development. The case example illus-
trates how the midwives learned not only by accessing online learning resources, but
through social interactions and active exchange of knowledge.
The integrative pedagogies model for developing professional expertise identifies
four types of knowledge needed for learning: (1) conceptual and theoretical knowl-
edge based on facts and concepts; (2) procedural or practical knowledge which
involves solving specialist, practical problems; and (3) sociocultural knowledge that
enables people to operate within a given cultural context; and (4) the self-regulative
knowledge needed to plan, perform and self-monitor development (Tynjälä et al.
2016). Formal education tends to focus on students learning conceptual and theoret-
ical knowledge as well as procedural and practical knowledge. Over past decades,
formal education has been expended to include opportunities to learn sociocultural
and self-regulative knowledge. MOOCs can continue this trajectory when they serve
as a focal point for the coordination of activities that support the development of all
four types of knowledge. As learners gain expertise, there is a qualitative change in
the way they use the resources in a MOOC to learn, moving from rule-based actions
to fluid, self-directed activities (Dreyfus and Dreyfus 2005). To support learning
of thee different types of knowledge, MOOCs have to be designed as participatory
spaces, rather than as a set of ‘learning materials’ and products in the conventional
sense. However, there has to be tolerance of learners who choose to participate in
different ways, as illustrated in the previous section.
Professional learning has been a growth area for MOOCs. The focus has been
on providing MOOCs for companies and public organisations. For example, the
UK’s tax office, Her Majesty’s Revenue and Customs (HMRC), offer MOOCs to
employees as a form of regular professional development. There are many growth
areas where MOOCs can aid professional learning. For example, combining work
and learning, as illustrated in the case example illustrating how midwives around
the world could share practice examples. Another potential growth area is the ‘gig-
economy’, companies such as Uber, Air B & B, and Mechanical Turk, where people
are paid per task and need to learn on a just-in-time basis (Nickerson 2013). Gig
economy workers could benefit, not only by using MOOC resources, but by partici-
pating in communities of networked expertise that could be associated with MOOCs.
There is lots of scope for MOOCs to disrupt, rather than replicate, forms of online
learning.
6.4 Opportunities for All: Supporting Self-regulation
MOOCs are positioned as a way for anyone, anywhere to access university education
in ways that are ‘equivalent to the on-campus experience’. The marketing documents
from the MOOC providers claim MOOCs open up universities to students globally
so they can become equal members of the academic community. This approach is
particularly appealing for people who would like to study at an elite university, but
have limited access to education. Nevertheless, there is a danger.
In Chap. 2, we described why learning online in a MOOC should not be viewed
as being equivalent or comparable to on-campus learning. The view of a MOOC
as being similar to a formal university ‘course’ places limitations on the benefits
of MOOCs for students and for society. Learners could be liberated from having to
follow a formal course pathway. And there are benefits for society when citizens can
identify gaps in their knowledge and actively pursue ways to fill these gaps.
Learning in a MOOC is qualitatively different from learning face-to-face in a
geographically based location and usually is not even equivalent to open, online
learning at scale at an Open University. A critical aspect of learning on campus or
at an open university is the support and feedback from tutors and peers, i.e. being
a part of an academic community. Open University modules and degrees have high
levels of support from tutors (academic support), and from student support teams
(pastoral and other support), which have been termed ‘supported open learning’.
Most universities offer tutor-based support and, crucially, students learn within a
community of scholars and peers. This form of support is missing or is truncated in
a MOOC.
To participate effectively in a MOOC, learners have to engage actively (although
not always collaborative). Chapter 4 provided ample evidence that not all learners
6.4 Opportunities for All: Supporting Self-regulation 103
are able (or want) to do this. Many do not have the cognitive, behavioural or affective
characteristics necessary to be active agents determining their own learning pathways
(Illeris 2007; Littlejohn et al. 2016). It seems MOOCs privilege those who are able
to plan, perform and self-regulate their learning. There is a danger that the expansion
of MOOCs inadvertently will lead to a form of discrimination, where those who are
unable or unwilling to direct their own learning will not have access to the teaching
support they require.
This disparity allows those who are able to self-regulate to overly influence what
is happening and what is being learned in the MOOC (Milligan et al. 2013). It
illustrates the ‘inequalities of participation’ Selwyn (2016, p. 31) warns of, where
the experiences and outcomes of participating in learning will differ considerably
depending on who the person is. If MOOCs are to be part of the shift towards
‘learnification’, where lifelong learners decide what, when and where they will learn,
a critical element that has to be taken into consideration is the ability of learners to
learn autonomously.
The ability to learn autonomously should be viewed as a critical literacy in a
world where open, online, learning is becoming significant. In the past, governments
have focused on critical literacies as a foundation of democracy and engagement in
society and should similarly take action ensure all citizens are able to self-regulate
their own learning in unstructured, online settings. There are a number of competency
frameworks that guide education (see for example Voogt and Roblin 2012). Some
frameworks emphasise self-regulation as a critical literacy. The expansion of MOOCs
and other forms of open, online education means that self-regulation will increase
in importance as a critical literacy. Otherwise, MOOCs and open, online education
will serve to exacerbate, rather than alleviate, the equity issues in education.
One problem is that providing opportunities for learners to develop self-regulation
ability can be complex and expensive. This is a particularly troublesome issue where
MOOCs are seen as a cost-effective way to educate the masses. However, online
learning should be valued for the unique ways it can support self-regulation through
social interactions (Nicol and Macfarlaine-Dick 2006). MOOCs could liberate learn-
ing by encouraging learners to self-determine their learning pathway, while support-
ing self-regulation. Therefore, it is crucial to move away from the narrow focus on
course provision and data-driven support towards preparing learners to be able to set
and follow their own ambitions in unstructured open, online environments.
6.5 Rethinking Success Measures
The introduction of MOOCs has been associated with forms of economic growth.
MOOCs may be viewed as a product that can be sold to student consumers. MOOCs
can also be considered a new form of ‘migration’, allowing people to study for degrees
in western universities, retaining the currency of a ‘western degree’ as superior to
degrees from other countries, rather than supporting the improvement of universities
around the world. Universities and businesses increasingly see MOOCs as part of a
new currency at the heart of generating income streams, where students buy resources
and qualifications. This may explain to some extent why MOOCs reinforce the idea
of trading educational resources and formal, undergraduate education, rather than
as a way to support societal learning in radically new ways. Tracing the evolving
business model that supports the MOOC platform provider FutureLearn exemplifies
these issues.
When the FutureLearn MOOC platform was introduced in 2012, it was based on
a ‘freemium’ model. The aim was to increase interest in the partner universities by
offering MOOCs as a taster and first step towards paid-for education. Although it is
clear that a well-designed MOOC can reinforce the value of a university’s ‘brand’,
the monetary benefits from follow-through registration are difficult to calculate, and
good return on investment is difficult to achieve. It is challenging to identify the
number of students who register and pay for a course after experiencing a MOOC
for free, since some of them may already have intended to study. The FutureLearn
business model is evolving. Along with partner universities Deakin (Australia) and
Coventry (UK), FutureLearn is currently experimenting with a new business model
that allows students to try taster courses for free, then register for MOOC-style
university degree programmes, as illustrated in the case example below:
Case Example: MOOCs as Deakin University Degrees

Deakin University in Australia is offering bachelor degrees on the Future-
Learn platform. Students can begin their study by participating in short ‘taster’
courses that are free of charge, before enrolling in the Bachelors programme
for a fee. The credits from the MOOC course go towards the degree. The
programme is comprised of sequences, short MOOCs with assessments at the
end of each course. FutureLearn describes this experience as ‘the equivalent
of a university subject’. Degrees are available in a range of subjects includ-
ing Cyber Security, Information Technology, Financial Planning, Humanitar-
ian and Development Action, Property and Diabetes Education. Deakin and
FutureLearn are not the first to offer MOOC-based degrees. Coursera, edX and
Udacity have all hosted Master’s level offerings. These degree-based MOOCs
have allowed universities and platform providers to experiment with revenue
generation and expand MOOC business models to include new business lines.
The perspective of a MOOC as a retail commodity available on demand to cus-

tomers does not take into consideration what is lost when learning solely is online,
in particular the role of in-person, social interaction with tutors and peers. There
are also ethical implications, especially transparency around what is being ‘sold’ to
students. Organisations need to be clear that the online learning experiences are not
equivalent to on-campus learning in terms of the qualitative experience.
It is not only the learners who need to understand what MOOCs do and do not
offer, employers also need to be made aware of what the new ‘currency’ of MOOC
6.5 Rethinking Success Measures 105
qualifications and ‘micro-credentialing’ signal. These achievements could be merely

a reinforcement and replication of traditional education; on the other hand, these new
forms of credentialing could be implemented in ways that are more democratic and
radically different from conventional education.
This view of MOOCs run the risk of narrowly focusing on success measures that
are based around the learner’s progress through a course—measures of progression,
retention, assessment scores and time in a digital learning platform. These measures
might not align with the learner’s intentions, especially if he or she wants to learn a
concept then leave the course. There is a danger that ‘automated detectors of affect
with nudges to promote growth mindset’ may result in attempts to quantify learners’
emotions and correct these to fit the ‘ideal’ psychological character. Numbers some-
times give an illusion of confidence, power and authority, whether their measures are
representative of complex learning situations or not.
Broader signifiers of success are being explored in the literature, such as learner
agency and the ability of learners to self-regulate their own learning. New analysis
techniques are being developed to examine whether and how participants learn in
online forums (Gillani and Eynon 2014), how they interact with intelligent tutoring
systems (Wen et al. 2014), their self-regulation patterns (Siadaty et al. 2012) and
their confidence and emotions (Dillon et al. 2016). The data from these analytics
techniques allow the development of automated scaffolds and prompts. However,
even these broad signifiers should be considered carefully because of complications
in assessing whether a scaffold supports better learning, since not every student wants
to reach the same endpoint. It is also difficult to pinpoint which factors actually are
influencing learning processes. Therefore, we have to be careful about the assump-
tions that underpin Artificial Intelligence (AI) and data-driven systems. Currently,
AI systems cannot assess learner progress at a level that is comparable with a human.
Therefore, a combination of automatic measurement and analysis along with self-
report and learner decision-making provides a possible way forward, though learners
need to have the ability to make decisions about their own learning based on these
multimodal data. Therefore, there are two future areas for the development of data-
analytics for MOOCs. First, we have to understand when are the critical moments
when scaffolding can help learners. But at the same time we must also make sure
learners have the decision-making skills to be able to use and act on analytics scaf-
folds. It is the human–computer interface that will make the biggest difference in the
effectiveness of MOOCs to support learners in achieving their goals.
This book has traced contradictions associated with the expansion of MOOCs. In
reconceptualising education as open, online learning, it is necessary to question not
only what new educational models are being implemented, but also why these models,
tools and processes are being introduced; how they will contribute to improvements
in practice; and how they will create enhanced opportunities and outcomes for all
learners. To fully understand these questions, it is necessary to look beyond MOOCs

themselves to explore the contexts that are shaping and informing their development
and design.
The democratising vision of MOOCs relates to Hardt and Negri’s (2005) con-
cept of ‘the multitude’, where large numbers of people self-organise within a net-
work to generate and share ‘common knowledge’ in ways that create conditions to
reduce oppressive forms of power. While an alluring idea, the evidence suggests that
MOOCs typically favour the educated elite, and that the democratising vision belies
the ‘inequalities of participation’ (Selwyn 2016, p. 31), and substantial variation in
the experiences and outcomes of individual learners. That is, MOOCs, and online
education more generally, struggles with the same issues of equity that ‘traditional’
education does.
Even when learners have the ability to learn autonomously, course designers
and researchers too often expect learners to conform to the course norms and spe-
cific behaviour (for example, completing a course or being ‘visible’). The systems
underpinning MOOCs continue to present a singular, top-down perspective of learn-
ing. Rather than emancipating the learner to follow a self-determined pathway, the
reliance on analytics-based scaffolds often subjugate learners into compliance rather
than supporting them to follow their own paths. However, despite the above pes-
simism, this book has identified examples of particular MOOCs that have served
to breakthrough some of the inequities facing education, for instance, for migrant
or refugee learners, or in brokering professional connections between midwives in
Europe and in Africa. These successes perhaps indicate that when utilised in partic-
ular contexts, for particular purposes and with particular populations, MOOCs do
have the potential to fulfil some of their original promise.
There, however, remains a risk that rather than offering a fresh, democratic
approach to education, MOOCs reproduce the tacit forms of control that underpin
education systems. At the same time, MOOCs also sustain the traditional hierarchy
within which the novice learner is subjugated to expert ‘teachers’ who work in a vari-
ety of roles: subject matter experts, course designers, data analysts and those who
create educational platforms and tools. There is a need to rethink ways MOOCs and
other forms of open, online learning can extend education not only within the nar-
row boundaries of formal education, but beyond these frontiers, in areas of informal,
professional, networked community-based learning. Again, there are nascent exam-
ples of these types of opportunities becoming available. Perhaps most promising
are the informal, self-organising groups and participatory learning opportunities that
would not be termed MOOCs but provide interesting case studies to understand how
access can be opened and learning becomes a more reciprocal process distributed
across users. In these instances, the open, distributed and collaborative possibilities
offered by the Internet are leveraged without the influence of formal or traditional
institutional structures.
Open, online learning has the potential to extend across every part of a learner’s
life. So, rather than focusing narrowly on how each learner fits within online edu-
cation, we must consider how this reconceptualisation of learning fits within each
learner’s lifecycle. Rather than concentrating on offering materials, courses and ser-
6.6 Concluding Thoughts 107
vices to the consumer student, we should take steps to ensure every learner has equal
opportunities to learn from and contribute to new emerging forms of open, online
learning. The ideas behind the ‘personalization’ movement in the compulsory sector
apply to MOOCs and other forms of online education. More problematic perhaps, is
that increasing evidence suggests that what makes personalization most successful
in schooling contexts is the presence of strong relational support networks to support
the student/learner through their learning journey.
These observations have a broader resonance with education in general, as
MOOCs become synonymous with almost any type of online learning. It is clear
that education systems, in their traditional forms, are not structured to facilitate the
range of learning opportunities that are required in the twenty-first century. MOOCs,
and open online learning in general, are providing exciting new models of learning.
However, as this book has explored, while these models create new opportunities, in
many cases they simply are reinforcing traditional educational models and outdated
hierarchies in education. It is vital to reconceptualise learning in the digital age to
harness the democratising potential of MOOCs.
Acknowledgements The authors wish to thank Vicky Murphy of The Open University for com-
ments and for proofing this chapter.
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SPRINGER BRIEFS IN

OPEN AND DISTANCE EDUC ATION

Open and Distance Education

ISSN 2211-1921 ISSN 2211-193X (electronic)

© The Author(s) 2018

Printed on acid-free paper

1 The Many Guises of MOOCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

3.2.1 The Student, the Learner . . . . . . . . . . . . . . . . . . . . .... 37

1.1 Introducing MOOCs

For many readers, MOOCs—massive, open, online courses—need no introduction.

1.2 MOOC Dimensions

New commercial models have been introduced. An increasing number of MOOCs

2 How does Coursera Make Money. Blogpost available from: https://www.edsurge.com/news/2014-

1.3 The Origins of MOOCs

1.4 Conceptualising What It Means to Be MOOC

The term MOOC is increasingly employed as a catchall phrase to denote a wide

1.5 Shifting Meanings: What Do Massive, Open, Online

Selwyn goes on to suggest that there is a:

Too often MOOCs are positioned as an autonomous, decontextualised learning activ-

1.10 MOOC Ideologies

Fig. 1.1 Common typologies of MOOCs

1.11 The Ambitions of This Book

Chapter 6 examines the broader societal dimensions fueling the expansion of

Abstract MOOCs have engendered excitement around their potential to democra-

2.1 The Hype, De-hype and Re-hype of MOOCs

© The Author(s) 2018 21

The early promise of MOOCs democratising education and providing future-focused,

2.2 The Learnification of Education; the Wider Context

to approaches to learning where the learner constructs knowledge, for example,

…. sense of the mismanagement of education by monolithic institutions that are profoundly

2.3 Towards Democracy

2.4 Different Challenges, Same Outcome

2.5 New Name, Repeating Model

MOOCs appear to be a response by the education sector and advocates of open

2.6 Concluding Thoughts

The theme of the [un]democratisation of MOOCs is returned to throughout this

3.1 Individual Learner, Common Challenges

MOOCs have the potential to provide as many different learning experiences as

© The Author(s) 2018 35

3.2 Student, Learner, User, Participant—Multiple Names

3.2.1 The Student, the Learner

3.2.2 The User, the Participant

‘User’ is a term frequently used in discussions of technology. The meaning attached

Social learning is an important characteristic of MOOCs. However, the plurality of

3.3 Why a MOOC? Motivations and Incentives Among

The democratising rhetoric surrounding MOOCs is acknowledged by Biesta (2009),

Fig. 3.1 A video-based lecture in the Fundamentals of Clinical Trials MOOC

Fig. 3.2 An e-text from the Fundamentals of Clinical Trials MOOC

A study of the ways learners self-regulate their learning in this Fundamentals of

By contrast, learners who perceived themselves as highly self-regulated learners

Fig. 3.3 Fundamentals of Clinical Trials MOOC online forum

self-regulators who participated in the MOOC to improve their work performance

However, learners reporting low self-regulation experienced difficulty in self-

This behaviour is similar to the conduct of low self-regulators in the Introduction to

compared with undergraduates or ‘leisure learners’, and will be attracted by differed

3.4 But Who Benefits?

3.5 A Closer Look at the Role of Self-regulated Learning

Self-regulated learning provides a theoretical means for accommodating the diver-

and self-identified needs. The motivations a learner brings to a particular MOOC,