Blurring Boundaries in Education: Context and Impact of MOOCs
Open accessAbstract
The opinions on MOOCs go to extremes, covering a wide variety of topics, affecting economy, pedagogy, and computer science which makes it hard to keep oversight. Despite the many excellent research reports and articles, an overview of the bigger picture, providing a holistic qualitative summary of the different opinions, is still very welcome. Special attention is given to the broader social, cultural, and technological context in which these developments come about. In this paper, it is discussed how the educational industry has received a “wake up call” provoking a global discussion on learning and teaching, accordingly disturbing established boundaries between formal and informal learning, public and for-profit education, teachers and learners and quite intriguingly, between software and teaching practices. Golden opportunities for Artificial Intelligence and Technology Enhanced Learning are unfolding, evidenced by the digitalization movement of education, gamification, and the stringent need for massively scalable (and therefore computerized) personal learning experiences.
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The challenge was largely attributed to weak pedagogical design and lack of appropriate assessment criteria.
“e challenge was largely attributed to the weak pedagogical design and lack of appropriate assessment criteria [8].”
From A Review of Artificial Intelligence (AI) in Education from 2010 to 2020 (2021) · cites this paper as [8]
The “Digital born courses” and “Concerns about Quality” sections describe shortcomings in MOOC course design and pedagogy. The “Scalable and reliable assessment” passage says assessments test skills superficially and are not yet trustworthy, but it does not specifically identify a lack of appropriate assessment criteria or say these two factors were largely responsible for the challenge. (LLM, conf=0.82)
Experimental — accuracy not yet measuredLearners could get access to historical events with authentic emotions in real-time interaction, and the collateral emotion would help them better understand the specific content knowledge.
“Learners could get access to historical events with authentic emotions in the real-time interaction, and the collateral emotion would help them better understand the specific content knowledge [8].”
From A Review of Artificial Intelligence (AI) in Education from 2010 to 2020 (2021) · cites this paper as [8]
The Gamification section (page unavailable) says learners can explore ancient societies, meet historical figures, and learn about history in a virtual world. It does not describe authentic emotions or say that emotions improve understanding; its reference to real-time interaction concerns exploring genetics, not historical events. (LLM, conf=0.87)
Experimental — accuracy not yet measuredThis would prevent the actualization of personalized learning experiences.
“is would prevent the actualization of personalized learning experiences [8,120].”
From A Review of Artificial Intelligence (AI) in Education from 2010 to 2020 (2021) · cites this paper as [8]
The Concerns about Quality section says standardized MOOCs lack personalized guidance, and Adaptive Learning and Creativity says personal learning experiences are barely present in MOOCs. These passages support a barrier to personalization, but not the stronger claim that it would be prevented altogether; the supplied sentence also omits what “This” refers to. No page numbers are available in our copy. (LLM, conf=0.82)
Experimental — accuracy not yet measured
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Cites
(1980) · Cited by 22,286 · see citing works
(2008) · Cited by 6,939 · see citing works
Christensen, C. M. (2011) · Vahlen eBooks · doi:10.15358/9783800642816 · Cited by 2,917 · see citing works
Distance education : a systems view
Moore, M. G., & Kearsley, G. (1996) · Medical Entomology and Zoology · Cited by 2,598 · see citing works
Confronting the Challenges of Participatory Culture
Jenkins, H., Purushotma, R., Weigel, M., Clinton, K., & Robison, A. J. (2009) · The MIT Press eBooks · doi:10.7551/mitpress/8435.001.0001 · Cited by 2,272 · see citing works
Educational Data Mining: A review of the state of the art
Romero, C., & Ventura, S. (2010) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2010.2053532 · Cited by 2,251 · see citing works
Cuban, L. (2009) · Harvard University Press eBooks · doi:10.2307/j.ctvk12qnw · Cited by 1,908 · see citing works
How video production affects student engagement: an empirical study of MOOC videos
Guo, P. J., Kim, J., & Rubin, R. (2014) · Proceedings of the first ACM conference on Learning @ scale conference · doi:10.1145/2556325.2566239 · Cited by 1,850 · see citing works
MOOCs: A systematic study of the published literature 2008-2012
Liyanagunawardena, T. R., Adams, A., & Williams, S. A. (2013) · The International Review of Research in Open and Distributed Learning · doi:10.19173/irrodl.v14i3.1455 · Cited by 1,093 · see citing works
Deconstructing disengagement: analyzing learner subpopulations in massive open online courses
Kizilcec, R. F., Piech, C., & Schneider, E. (2013) · Proceedings of the Third International Conference on Learning Analytics and Knowledge · doi:10.1145/2460296.2460330 · Cited by 984 · see citing works
(2014) · Choice Reviews Online · doi:10.5860/choice.186403 · Cited by 910 · see citing works
MOOCs and Open Education: Implications for Higher Education
Li, Y., & Powell, S. (2013) · e-space (Manchester Metropolitan University) · doi:10.13140/2.1.5072.8320 · Cited by 770 · see citing works
Building Intelligent Interactive Tutors : student-centered strategies for revolutionizing e-learning
Woolf, B. P. (2009) · HAL · Cited by 716 · see citing works
Kim, K. H. (2011) · Creativity Research Journal · doi:10.1080/10400419.2011.627805 · Cited by 709 · see citing works
Education As Cultural Imperialism
Wiggins, T. W., & Carnoy, M. (1975) · American Educational Research Journal · doi:10.2307/1162754 · Cited by 701 · see citing works
The Impact of Self- and Peer-Grading on Student Learning
Sadler, P., & Good, E. (2006) · Educational Assessment · doi:10.1207/s15326977ea1101_1 · Cited by 535 · cited 1× · see citing works
“Furthermore, evidence seem to indicate that students learn from these kinds of grading (Sadler & Good, 2006).”
BackgroundHow Video Production Affects Student Engagement:An Empirical Study of MOOC Videos
McVerry, G. (2019) · Cited by 436 · see citing works
Oversold and underused: computers in the classroom
(2002) · Choice Reviews Online · doi:10.5860/choice.39-2921 · Cited by 434 · see citing works
John-Steiner, V. P. (2006) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195307702.001.0001 · Cited by 433 · see citing works
Innovating Pedagogy 2015: Open University Innovation Report 4
Sharples, M., Adams, A., Alozie, N. M., Ferguson, R., FitzGerald, E., Gaved, M., McAndrew, P., Means, B., Remold, J., Rienties, B., et al. (2015) · Open Research Online - ORO (The Open University) · Cited by 364 · see citing works
MOOCs: Expectations and Reality
Tirthali, F. M. H. A. D. (2014) · Scholarly Commons (University of Pennsylvania) · Cited by 303 · see citing works
MOOCs: So Many Learners, So Much Potential ...
Kay, J., Reimann, P., Diebold, E., & Kummerfeld, B. (2013) · IEEE Intelligent Systems · doi:10.1109/mis.2013.66 · Cited by 273 · see citing works
The Future of Learning Institutions in a Digital Age
Davidson, C. N., Goldberg, D. T., & Jones, Z. (2009) · The MIT Press eBooks · doi:10.7551/mitpress/8517.001.0001 · Cited by 240 · see citing works
Rethinking Teaching for the Knowledge Society
Laurillard, D. (2002) · Cited by 215 · see citing works
Recommender Systems for Learning
Manouselis, N., Drachsler, H., Verbert, K., et al. (2012) · Springer briefs in electrical and computer engineering · doi:10.1007/978-1-4614-4361-2 · Cited by 186 · see citing works
Grouping and Achievement in Cooperative Learning
Baer, J. (2003) · College Teaching · doi:10.1080/87567550309596434 · Cited by 116 · see citing works
A Review of e-Learning in Canada: A Rough Sketch of the Evidence, Gaps and Promising Directions
Abrami, P. C., Bernard, R. M., Wade, A., Schmid, R. F., Borokhovski, E., Tamin, R., Surkes, M. A., Lowerison, G., Zhang, D., Nicolaidou, I. A., et al. (2008) · Canadian Journal of Learning and Technology · doi:10.21432/t2qs3k · Cited by 111 · see citing works
What's Right and What's Wrong about Coursera-Style MOOCs
Bates, T. (2019) · Cited by 48 · see citing works
The MOOC Misstep and the Open Education Infrastructure
Wiley, D. A. (2015) · Routledge eBooks · doi:10.4324/9781315751108-2 · Cited by 24 · see citing works
Prabakaran, J. (2014) · National Journal of Research in Community Medicine · doi:10.26727/njrcm.2014.3.2.100-102 · Cited by 21 · see citing works
Fisher, A., Anderson, G. B., Peng, R. D., et al. (2014) · PeerJ · doi:10.7717/peerj.589 · Cited by 13 · see citing works
The Creative Society of the 21st Century
OECD (2000) · OECD eBooks · doi:10.1787/9789264182479-en · Cited by 13 · see citing works
The future of learning : issues and prospects
所, 真., & Steels, L. (2003) · IOS Press , Distributor in Japan, Ohmsha eBooks · Cited by 11 · see citing works
The Online Revolution: Education at Scale
Ng, A., & Koller, D. (2012) · Cited by 8 · see citing works
Fisher, A., Anderson, G. B., Peng, R. D., et al. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1404.5358 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 8
- Bloom, B.S. (1968). Learning for mastery. Evaluation Comment, 1(2), 1-11. Retrieved from http://programs.honolulu.hawaii.edu/intranet/sites/programs.honolulu.hawaii.edu.intranet/file s/upstfstudent-success-bloom-1968.pdf.
- Dodd, T. (2014b, October 6). Top MOOC provider edX no longer free for all [article]. Financial Review. http://www.afr.com/p/national/education/top_mooc_provider_edx_no_longer_FooMSmV3Ld SQHYGKND4LoI.
- Haber, J. (2014c, September 2014). MOOCs. Cambridge, MA: MIT Press.
- Hepler, L. (2013, November 29). Can online education grow up? [Blog post] Retrieved from http://www.bizjournals.com/sanjose/print-edition/2013/11/29/can-onlineeducation-grow- up.html.
- IBM 2010 Global CEO Study: Creativity Selected as Most Crucial Factor for Future Success [Report] (2010, May 18). Retrieved from http://www-03.ibm.com/press/us/en/pressrelease/31670.wss.
- Koller, D. (2012). What we're learning from online education [presentation]. TED Talk, Retrieved https://www.ted.com/talks/daphne_koller_what_we_re_learning_from_online_education?lang uage=nl KVAB: Final conference on blended learning [website]. (2014, November 19). Retrieved from http://www.kvab.be/denkersprogramma/conference-blended-learning.aspx
- Lowendahl, J.-M. (2014). Your university of the future -Executing on the higher education business model scenarios [Tech. Rep.]. Gartner. Retrieved from http://www.eunis.org/eunisrectors14/wpcontent/uploads/sites/5/2014/04/Lowendahl-Eunis- rectors-conference-Official.pdf.
- Moe, R. (2014, May 15). The MOOC problem. Retrieved from http://www.hybridpedagogy.com/journal/mooc-problem/.
Cited by
Sorted by citations
A Review of Artificial Intelligence (AI) in Education from 2010 to 2020
Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., Liu, J.-B., Yuan, J., & Li, Y. (2021) · Complexity · doi:10.1155/2021/8812542 · Cited by 1,364 · see citing works
“To date, some studies have reported the lack of significant impact on improving teaching. challenge was largely attributed to the weak pedagogical design and lack of appropriate assessment criteria [8].”
BackgroundInfluentialChen, A., Zhang, Y., Jia, J., Liang, M., Cha, Y., & Lim, C. P. (2025) · Journal of Computer Assisted Learning · doi:10.1111/jcal.13064 · Cited by 67 · see citing works
Artificial Intelligence in Health Professions Education
Shankar, P. R. (2022) · Archives of Medicine and Health Sciences · doi:10.4103/amhs.amhs_234_22 · Cited by 40 · see citing works
- Authors
- Loeckx, J.
- Affiliations
- Vrije Universiteit Brussel (BE)
- Corresponding author
- Loeckx, Johan
- ORCID
- Loeckx, Johan
- Venue
- The International Review of Research in Open and Distributed Learning
- Publisher
- Athabasca University Press
- Published
- 2016-05-16
- Volume
- 17
- Issue
- 3
- ISSN
- 1492-3831 (ISSN-L)
- OpenAlex
- W2402444196
- Cited by
- 86
- References
- 35
- Citations by year
- 2026: 7 · 2025: 14 · 2024: 19 · 2023: 17 · 2022: 9 · 2021: 7 · 2020: 5 · 2019: 3 · 2018: 2 · 2017: 2 · 2016: 1
- FWCI
- 3.56
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- The International Review of Research in Open and Distributed Learning (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Érudit (Université de Montréal) (submittedVersion)
- VUBIR (Vrije Universiteit Brussel) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Information Systems › Digital Education and Society
- Physical Sciences › Computer Science › Computer Science Applications › Open Education and E-Learning
- Keywords
- MOOCs, massive open online courses, formal and informal learning, for-profit education, technology-enhanced learning, artificial intelligence in education, gamification, digitalization of education, education sector, online learning
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