research-article

Learning Transfer: Does It Take Place in MOOCs? An Investigation into the Uptake of Functional Programming in Practice

Authors:

Guanliang Chen,

Geert-Jan HoubenAuthors Info & Claims

L@S '16: Proceedings of the Third (2016) ACM Conference on Learning @ Scale

Pages 409 - 418

https://doi.org/10.1145/2876034.2876035

Published: 25 April 2016 Publication History

Abstract

The rising number of Massive Open Online Courses (MOOCs) enable people to advance their knowledge and competencies in a wide range of fields. Learning though is only the first step, the transfer of the taught concepts into practice is equally important and often neglected in the investigation of MOOCs. In this paper, we consider the specific case of FP101x (a functional programming MOOC on edX) and the extent to which learners alter their programming behaviour after having taken the course. We are able to link about one third of all FP101x learners to GitHub, the most popular social coding platform to date and contribute a first exploratory analysis of learner behaviour beyond the MOOC platform. A detailed longitudinal analysis of GitHub log traces reveals that (i) more than 8% of engaged learners transfer, and that (ii) most existing transfer learning findings from the classroom setting are indeed applicable in the MOOC setting as well.

References

[1]

Adams, L. T., Kasserman, J. E., Yearwood, A., Perfetto, G. A., Bransford, J. D., and Franks, J. J. Memory access: The effects of fact-oriented versus problem-oriented acquisition. Memory & Cognition 16, 2 (1988), 167--175.

[2]

Baldwin, T. T., and Ford, K. J. Transfer of training: A review and directions for future research. Personnel Psychology 41, 1 (1988), 63--105.

[3]

Bandura, A. Self-efficacy: toward a unifying theory of behavioral change. Psychological review 84, 2 (1977), 191--215.

[4]

Barnett, S. M., and Ceci, S. J. When and where do we apply what we learn? a taxonomy for far transfer. Psychological bulletin 128, 4 (2002), 612--637.

[5]

Bjork, R. A. Memory and metamemory considerations in the training of human beings. Metacognition: Knowing about knowing (1994), 185--205.

[6]

Bjork, R. A., and Bjork, E. L. A new theory of disuse and an old theory of stimulus fluctuation. From learning processes to cognitive processes: Essays in honor of William K. Estes 2 (1992), 35--67.

[7]

Blume, B. D., Ford, K. J., Baldwin, T. T., and Huang, J. L. Transfer of training: A meta-analytic review. Journal of Management 36, 4 (2010), 1065--1105.

[8]

Chen, G., Gully, S. M., and Eden, D. Validation of a new general self-efficacy scale. Organizational research methods 4, 1 (2001), 62--83.

[9]

Darnon, C., Butera, F., and Harackiewicz, J. M. Achievement goals in social interactions: Learning with mastery vs. performance goals. Motivation and Emotion 31, 1 (2007), 61--70.

[10]

DeBoer, J., Ho, A. D., Stump, G. S., and Breslow, L. Changing "course": Reconceptualizing educational variables for massive open online courses. Educational Researcher 43, 2 (2014), 74--84.

[11]

Donovan, J. J., and Radosevich, D. J. A meta-analytic review of the distribution of practice effect: Now you see it, now you don't. Journal of Applied Psychology 84, 5 (1999), 795--805.

[12]

Fidalgo-Blanco, Á., Sein-Echaluce, M. L., Garcia-Penalvo, F. J., and Escano, J. E. Improving the mooc learning outcomes throughout informal learning activities. In TEEM'14 (2014), 611--617.

Digital Library

[13]

Ford, K. J., Quinones, M. A., Sego, D. J., and Sorra, J. S. Factors affecting the opportunity to perform trained tasks on the job. Personnel Psychology 45, 3 (1992), 511--527.

[14]

Hill, T., Smith, N. D., and Mann, M. F. Role of efficacy expectations in predicting the decision to use advanced technologies: The case of computers. Journal of Applied Psychology 72, 2 (1987), 307--313.

[15]

Ho, A. D., Chuang, I., Reich, J., and Coleman phet al., C. A. Harvardx and mitx: Two years of open online courses fall 2012-summer 2014. SSRN 2586847 (2015).

[16]

Holton III, E. F., Bates, R. A., and Ruona, W. E. Development of a generalized learning transfer system inventory. Human resource development quarterly 11, 4 (2000), 333--360.

[17]

Koedinger, K. R., Corbett, A. T., and Perfetti, C. The knowledge-learning-instruction framework: Bridging the science-practice chasm to enhance robust student learning. Cognitive Science 36, 5 (2012), 757--798.

[18]

Koller, D., Ng, A., Do, C., and Chen, Z. Retention and intention in massive open online courses. Educause Review 48, 3 (2013), 62--63.

[19]

Lim, D. H., and Johnson, S. D. Trainee perceptions of factors that influence learning transfer. International journal of training and development 6 (2002), 36--48.

[20]

Lockhart, R. S., Lamon, M., and Gick, M. L. Conceptual transfer insimple insight problems. Memory & Cognition 16, 1 (1988), 36--44.

[21]

Miyamoto, Y. R., Coleman, C. A., Williams, J. J., Whitehill, J., Nesterko, S. O., and Reich, J. Beyond time-on-task: The relationship between spaced study and certification in moocs. SSRN 2547799 (2015).

[22]

Pugh, K. J., and Bergin, D. A. Motivational influences on transfer. Educational Psychologist 41, 3 (2006), 147--160.

[23]

Rodriguez, O. The concept of openness behind c and x-moocs (massive open online courses). Open Praxis 5, 1 (2013), 67--73.

[24]

Saks, A. M., and Belcourt, M. An investigation of training activities and transfer of training in organizations. Human resource management 45, 4 (2006), 629--648.

[25]

Thorndike, E. L. The psychology of learning. Educational Psychology. Teachers College, Columbia University, 1913.

[26]

Veletsianos, G., Collier, A., and Schneider, E. Digging deeper into learners' experiences in moocs: Participation in social networks outside of moocs, notetaking and contexts surrounding content consumption. British Journal of Educational Technology 46, 3 (2015), 570--587.

[27]

Wang, Y., Paquette, L., and Baker, R. A longitudinal study on learner career advancement in moocs. Journal of Learning Analytics 1, 3 (2014), 203--206.

[28]

Willingham, D. T. What will improve a student's memory? American Educator 32, 4 (2008), 17--25.

[29]

Yelon, S. L., and Ford, K. J. Pursuing a multidimensional view of transfer. Performance Improvement Quarterly 12, 3phantomxxx (1999), 58--78.

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Ghosh SSarkar SRoy PRoy BPodder A(2024)Role of Libraries in Promoting Digital Literacy and Information FluencyNavigating AI in Academic Libraries10.4018/979-8-3693-3053-1.ch005(77-108)Online publication date: 11-Oct-2024
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Schettino GHodačová LCaso DCapone V(2024)Physicians’ adoption of massive open online courses content in the workplaceComputers in Human Behavior10.1016/j.chb.2024.108151154:COnline publication date: 25-Jun-2024
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Index Terms

Learning Transfer: Does It Take Place in MOOCs? An Investigation into the Uptake of Functional Programming in Practice
1. Social and professional topics
  1. Professional topics
    1. Computing education
      1. Computing education programs
        Computer science education

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cover image ACM Conferences

L@S '16: Proceedings of the Third (2016) ACM Conference on Learning @ Scale

April 2016

446 pages

ISBN:9781450337267

DOI:10.1145/2876034

General Chair:
Jeff Haywood
The University of Edinburgh, UK
,
Program Chairs:
Vincent Aleven
Carnegie Mellon University, USA
,
Judy Kay
University of Sydney, Australia
,
Ido Roll
University of British Columbia, Canada

Copyright © 2016 ACM.

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Published: 25 April 2016

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L@S 2016: Third (2016) ACM Conference on Learning @ Scale

April 25 - 26, 2016

Scotland, Edinburgh, UK

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L@S '16 Paper Acceptance Rate 18 of 79 submissions, 23%;

Overall Acceptance Rate 117 of 440 submissions, 27%

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Cited By

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https://doi.org/10.4018/979-8-3693-5538-1.ch015
Ghosh SSarkar SRoy PRoy BPodder A(2024)Role of Libraries in Promoting Digital Literacy and Information FluencyNavigating AI in Academic Libraries10.4018/979-8-3693-3053-1.ch005(77-108)Online publication date: 11-Oct-2024
https://doi.org/10.4018/979-8-3693-3053-1.ch005
Schettino GHodačová LCaso DCapone V(2024)Physicians’ adoption of massive open online courses content in the workplaceComputers in Human Behavior10.1016/j.chb.2024.108151154:COnline publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1016/j.chb.2024.108151
Duncan ARusch ARavi PJoyner DSpikol DViberg OMartínez-Monés AGuo P(2023)The L@St Eight Years: A Review of Papers and Authors at Learning @ ScaleProceedings of the Tenth ACM Conference on Learning @ Scale10.1145/3573051.3596192(383-387)Online publication date: 20-Jul-2023
https://dl.acm.org/doi/10.1145/3573051.3596192
Li YKrasny M(2022)Network Climate Action Through MOOCsSocial and Emotional Learning and Complex Skills Assessment10.1007/978-3-031-06333-6_15(311-329)Online publication date: 25-Aug-2022
https://doi.org/10.1007/978-3-031-06333-6_15
Säde MSuviste RLuik P(2021)Measuring the Post-Impact of Programming MOOCs: Development and Validation of an InstrumentEducation Sciences10.3390/educsci1112081111:12(811)Online publication date: 14-Dec-2021
https://doi.org/10.3390/educsci11120811
Cho JLi YArmstrong ARuss AKrasny MKizilcec RMeinel CPérez-Sanagustín MSpecht MOgan A(2021)Using Social Norms to Promote Actions Beyond the CourseProceedings of the Eighth ACM Conference on Learning @ Scale10.1145/3430895.3460144(161-172)Online publication date: 8-Jun-2021
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Liu HZhang TLi FGu YYu G(2021)Tracking Knowledge Structures and Proficiencies of Students With Learning TransferIEEE Access10.1109/ACCESS.2020.30321419(55413-55421)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2020.3032141
Luik PLepp MFeklistova LSäde MRõõm MPalts TSuviste RTõnisson E(2020)Programming MOOCs – different learners and different motivationInternational Journal of Lifelong Education10.1080/02601370.2020.1780329(1-14)Online publication date: 18-Jun-2020
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Kizilcec RReich JYeomans MDann CBrunskill ELopez GTurkay SWilliams JTingley D(2020)Scaling up behavioral science interventions in online educationProceedings of the National Academy of Sciences10.1073/pnas.1921417117117:26(14900-14905)Online publication date: 15-Jun-2020
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