Rebooting MOOC Research
Abstract
Improve assessment, data sharing, and experimental design
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
MOOC platforms have not yet made learning behaviour data across multiple courses accessible to AI researchers.
“As MOOC platforms (e.g. Coursera, edX) have not yet made learning behaviour data across multiple courses accessible to AI researchers,9”
From Towards AI-Powered Personalization in MOOC Learning (2017) · cites this paper as [9]
Existing AI research in this area mostly relies on course-specific datasets released in an ad-hoc manner.
“existing AI research in this area mostly relies on course-specific datasets released in an ad-hoc manner (e.g. the KDD Cup 2015 competition dataset).”
From Towards AI-Powered Personalization in MOOC Learning (2017) · cites this paper as [9]
Machine learning-based learner assessment techniques mainly reflect how students interact with, rather than learn from, the courses.
“Using data derived from interaction mechanisms currently available in MOOC platforms, machine learning-based learner assessment techniques mainly reflect how students interact with, rather than learn from, the courses.9,26,27”
From Towards AI-Powered Personalization in MOOC Learning (2017) · cites this paper as [9]
Cites
Confusion can be beneficial for learning.
D'Mello, S. K., Lehman, B. A., Pekrun, R., & Graesser, A. (2014) · Learning and Instruction · doi:10.1016/j.learninstruc.2012.05.003 · Cited by 782 · see citing works
The microgenetic method: A direct means for studying cognitive development.
Siegler, R. S., & Crowley, K. (1991) · American Psychologist · doi:10.1037/0003-066x.46.6.606 · Cited by 683 · see citing works
Learning to think like a physicist: A review of research-based instructional strategies
Heuvelen, A. V. (1991) · American Journal of Physics · doi:10.1119/1.16667 · Cited by 528 · see citing works
Emanuel, E. (2013) · Nature · doi:10.1038/503342a · Cited by 209 · see citing works
Privacy, anonymity, and big data in the social sciences
Daries, J. P., Reich, J., Waldo, J., Young, E. M., Whittinghill, J., Ho, A. D., Seaton, D. T., & Chuang, I. L. (2014) · Communications of the ACM · doi:10.1145/2643132 · Cited by 126 · see citing works
Self-citationLearning in an introductory physics MOOC: All cohorts learn equally, including an on-campus class
Colvin, K. F., Champaign, J., Liu, A., Zhou, Q., Fredericks, C., & Pritchard, D. E. (2014) · The International Review of Research in Open and Distributed Learning · doi:10.19173/irrodl.v15i4.1902 · Cited by 91 · see citing works
Raising cognitive load with linear multimedia to promote conceptual change
Muller, D. A., Sharma, M. D., & Reimann, P. (2008) · Science Education · doi:10.1002/sce.20244 · Cited by 82 · see citing works
The Journal of Learning Analytics: Supporting and Promoting Learning Analytics Research
Siemens, G. (2014) · Journal of Learning Analytics · doi:10.18608/jla.2014.11.2 · Cited by 33 · see citing works
Heroesx: The Ancient Greek Hero: Spring 2013 Course ReportPreprint
Reich, J., Emanuel, J., Nesterko, S., et al. (2014) · SSRN Electronic Journal · doi:10.2139/ssrn.2382246 · Cited by 16 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Kizilcec, R. F., Pérez-Sanagustín, M., & Maldonado, J. J. (2017) · Computers & Education · doi:10.1016/j.compedu.2016.10.001 · Cited by 1,003 · see citing works
Baker, R. S., & Hawn, A. (2022) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-021-00285-9 · Cited by 993 · see citing works
How Do We Model Learning at Scale? A Systematic Review of Research on MOOCs
Joksimović, S., Poquet, O., Kovanović, V., Dowell, N., Mills, C., Gašević, D., Dawson, S., Graesser, A. C., & Brooks, C. (2018) · Review of Educational Research · doi:10.3102/0034654317740335 · Cited by 182 · see citing works
Towards AI-Powered Personalization in MOOC Learning
Yu, H., Miao, C., Leung, C., & White, T. J. (2017) · npj Science of Learning · doi:10.1038/s41539-017-0016-3 · Cited by 131 · see citing works
Data science in education: big data and learning analytics.
Klašnja‐Milićević, A., Ivanović, M., & Budimac, Z. (2017) · Computer Applications in Engineering Education · doi:10.1002/cae.21844 · Cited by 121 · see citing works
Educational scalability in MOOCs: Analysing instructional designs to find best practices
Julia, K., Peter, V. R., & Marco, K. (2021) · Computers & Education · doi:10.1016/j.compedu.2020.104054 · Cited by 94 · see citing works
Data-driven detection and characterization of communities of accounts collaborating in MOOCs
Ruipérez-Valiente, J. A., Jaramillo-Morillo, D., Joksimović, S., Kovanović, V., Muñoz-Merino, P. J., & Gašević, D. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.07.003 · Cited by 26 · see citing works
- Authors
- Reich, J.
- Affiliations
- Harvard University (US)
- Corresponding author
- Reich, Justin
- ORCID
- Reich, Justin
- Venue
- Science
- Published
- 2015-01-02
- Volume
- 347
- Issue
- 6217
- Pages
- 34-35
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2008932834
- Cited by
- 294
- References
- 9
- Citations by year
- 2026: 12 · 2025: 7 · 2024: 5 · 2023: 9 · 2022: 26 · 2021: 36 · 2020: 20 · 2019: 42 · 2018: 40 · 2017: 38 · 2016: 44 · 2015: 15
- FWCI
- 68.67
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Computer Science Applications › Open Education and E-Learning
- Social Sciences › Decision Sciences › Information Systems and Management › Academic Publishing and Open Access
- Keywords
- MOOCs, massive open online courses, research methodology, assessment, data sharing, experimental design
- MeSH terms
- Humans, Learning, Online Systems, Research Design, Education, Distance, Access to Information, Information Dissemination
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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