Work in Progress

Personalization at Scale: Making Learning Personally Relevant in a Climate Science MOOC

Authors:

Ido Roll,

Ilana Ram,

Sara HarrisAuthors Info & Claims

L@S '21: Proceedings of the Eighth ACM Conference on Learning @ Scale

Pages 263 - 266

https://doi.org/10.1145/3430895.3460154

Published: 08 June 2021 Publication History

Get Access

Abstract

Personalization and choice in learning activities can increase student engagement, satisfaction, and learning gains. But does this effect hold when implemented at scale? The current work explores the effects of personalization and learner's choice in a Climate Science MOOC. We manipulated these by creating two versions of course assignments. Learners who completed the assignments (N=219) received either Generic assignments focusing on global climate issues or Personalized assignments in which learners explored their own regions. Following the manipulation, learners in the Personalization group reported equal understanding of both Global and Local climate issues while learners in the Generic group reported better understanding of global issues and reduced understanding of local issues. Further, personalization did not affect interest or assignment length. We describe opportunities for personalization at scale and discuss their outcomes.

References

[1]

A Shukor, N., & Abdullah, Z. (2019). Using Learning Analytics to Improve MOOC Instructional Design. International Journal of Emerging Technologies in Learning (IJET), 14(24), 6. https://doi.org/10.3991/ijet.v14i24.12185

Crossref

Google Scholar

[2]

Cordova, D. I., & Lepper, M. R. (1996). Intrinsic motivation and the process of learning: Beneficial effects of contextualization, personalization, and choice. Journal of educational psychology, 88(4), 715.

Crossref

Google Scholar

[3]

Kizilcec, R. F., Davis, G. M., & Cohen, G. L. (2017). Towards Equal Opportunities in MOOCs: Affirmation Reduces Gender & Social-Class Achievement Gaps in China. Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale, 121--130. https://doi.org/10.1145/3051457.3051460

Digital Library

Google Scholar

[4]

Pardo, A., Jovanovic, J., Dawson, D., & Mirriahi, N. (2019). Using learning analytics to scale the provision of personalised feedback: Learning analytics to scale personalised feedback. British Journal of Educational Technology, 50(1), 128--138. https://doi.org/10.1111/bjet.12592

Crossref

Google Scholar

[5]

Roll, I., Russell, D. M., &, D. (2018). Learning at scale. International Journal of Artificial Intelligence in Education, 28(4), 471--477.

Crossref

Google Scholar

[6]

Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26(2), 582--599.

Crossref

Google Scholar

[7]

Walkington, C., & Bernacki, M. L. (2014). Motivating Students by "Personalizing" Learning around Individual Interests: A Consideration of Theory, Design, and Implementation Issues. In Motivational Interventions (Vol. 18, pp. 139--176). Emerald Group Publishing Limited. https://doi.org/10.1108/S0749--742320140000018004

Crossref

Google Scholar

[8]

Watson, S. L., Loizzo, J., Watson, W. R., Mueller, C., Lim, J., & Ertmer, P. A. (2016). Instructional design, facilitation, and perceived learning outcomes: An exploratory case study of a human trafficking MOOC for attitudinal change. Educational Technology Research and Development, 64(6), 1273--1300. https://doi.org/10.1007/s11423-016--9457--2

Crossref

Google Scholar

[9]

Pintaric Zorka Novak & Kravanja Zdravko. (2020). The Impact of the COVID-19 Pandemic in 2020 on the Quality of STEM Higher Education. Chemical Engineering Transactions, 81, 1315--1320. https://doi.org/10.3303/CET2081220

Crossref

Google Scholar

Cited By

View all

Ceglie R(2024)Science ResistanceGlobal Challenges for the Environment and Climate Change10.4018/979-8-3693-2845-3.ch012(212-230)Online publication date: 28-Jun-2024
https://doi.org/10.4018/979-8-3693-2845-3.ch012
Wengrowicz NLavi RKohen HDori D(2022)Modeling with Real-Time Informative Feedback: Implementing and Evaluating a New Massive Open Online Course ComponentJournal of Science Education and Technology10.1007/s10956-022-10019-832:6(884-897)Online publication date: 22-Dec-2022
https://doi.org/10.1007/s10956-022-10019-8

Index Terms

Personalization at Scale: Making Learning Personally Relevant in a Climate Science MOOC
1. Applied computing
  1. Education
    1. E-learning

Recommendations

Motivation and Engagement in MOOCs: How to Increase Learning Motivation by Adapting Pedagogical Scenarios?
Design for Teaching and Learning in a Networked World
Abstract
With the MOOC movement, education stakeholders are taking part of a pedagogical renewal. These dynamics present different advantages such as: massiveness, openness, accessibility, etc. However, it raises particular issues related to the dropout. ...
Personalization in OELEs: Developing a Data-Driven Framework to Model and Scaffold SRL Processes
Artificial Intelligence in Education
Abstract
This research focuses on developing a data-driven framework for modeling and scaffolding learners’ self-regulated learning (SRL) processes in open-ended learning environments (OELE). The aim of this work is to offer a personalized and productive ...
Incorporating customization and personalization into game-based learning

In recent years, customization and personalization were widely applied to accommodate the needs of different cognitive style groups. Such two approaches have different advantages and disadvantages but there is a lack of studies to compare these two ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

L@S '21: Proceedings of the Eighth ACM Conference on Learning @ Scale

June 2021

380 pages

ISBN:9781450382151

DOI:10.1145/3430895

General Chair:
Christoph Meinel
Hasso Plattner Institute for Digital Engineering Potsdam, Germany
,
Program Chairs:
Mar Pérez-Sanagustín
Paul Sabatier University Toulouse III, France
,
Marcus Specht
TU Delft, Netherlands
,
Amy Ogan
Carnegie Mellon University, USA

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 June 2021

Check for updates

Author Tags

Qualifiers

Work in progress

Conference

L@S '21

L@S '21: Eighth (2021) ACM Conference on Learning @ Scale

June 22 - 25, 2021

Virtual Event, Germany

Acceptance Rates

Overall Acceptance Rate 117 of 440 submissions, 27%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
101
Total Downloads

Downloads (Last 12 months)12
Downloads (Last 6 weeks)2

Reflects downloads up to 09 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Ceglie R(2024)Science ResistanceGlobal Challenges for the Environment and Climate Change10.4018/979-8-3693-2845-3.ch012(212-230)Online publication date: 28-Jun-2024
https://doi.org/10.4018/979-8-3693-2845-3.ch012
Wengrowicz NLavi RKohen HDori D(2022)Modeling with Real-Time Informative Feedback: Implementing and Evaluating a New Massive Open Online Course ComponentJournal of Science Education and Technology10.1007/s10956-022-10019-832:6(884-897)Online publication date: 22-Dec-2022
https://doi.org/10.1007/s10956-022-10019-8

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Motivation and Engagement in MOOCs: How to Increase Learning Motivation by Adapting Pedagogical Scenarios?

Personalization in OELEs: Developing a Data-Driven Framework to Model and Scaffold SRL Processes

Incorporating customization and personalization into game-based learning

Comments

Information

Published In

Publisher

Publication History

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations