Correlating skill and improvement in 2 MOOCs with a student's time on tasks
Abstract
Because MOOCs offer complete logs of student activities for each student there is hope that it may be possible to find out which activities are the most useful for learning. We start this quest by examining correlations between time spent on specific course resources and various measures of student performance: score on assessments, skill as defined by Item Response Theory, improvement in skill over the period of the course, and conceptual improvement as measured by a pre-post test. We study two MOOCs offered on edX.org by MIT faculty: Circuits and Electronics (6.002x) and Mechanics Review (8.MReV). Surprisingly, we find strong negative correlations in 6.002x between student skill and resource use; we attribute these findings to the fact that students with higher initial skills can do the exercises faster and with less time spent on instructional resources. We find weak or slightly negative correlations between relative improvement and resource use in 6.002x. The correlations with learning are stronger for conceptual knowledge in 8.MReV than with relative improvement, but similar for all course activities (except that eText checkpoint questions correlate more strongly with relative improvement). Clearly, the wide distribution of demographics and initial skill in MOOCs challenges us to isolate the habits of learning and resource use that correlate with learning for different students.
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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 [26]
Cites
Fundamentals of item response theory
(1992) · Choice Reviews Online · doi:10.5860/choice.29-4185 · Cited by 3,417 · see citing works
Cognitive Tutors: Lessons Learned
Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995) · Journal of the Learning Sciences · doi:10.1207/s15327809jls0402_2 · Cited by 1,982 · see citing works
Montaño, J., Toscano, J. J., Ramírez–Zavaleta, F., & Flores-Tlalpa, A. (2011) · conference · Cited by 981 · see citing works
Samejima, F. (1997) · Springer eBooks · doi:10.1007/978-1-4757-2691-6_5 · Cited by 946 · see citing works
Adams, W. K., Perkins, K. K., Podolefsky, N. S., Dubson, M., Finkelstein, N. D., & Wieman, C. (2006) · Physical Review Special Topics - Physics Education Research · doi:10.1103/physrevstper.2.010101 · Cited by 697 · see citing works
Proceedings of the 7th International Conference on Educational Data Mining.
Stamper, J. C., Pardos, Z. A., Mavrikis, M., & McLaren, B. M. (2014) · Educational Data Mining · Cited by 667 · see citing works
Who does what in a massive open online course?
Seaton, D. T., Bergner, Y., Chuang, I. L., Mitros, P., & Pritchard, D. E. (2014) · Communications of the ACM · doi:10.1145/2500876 · Cited by 236 · see citing works
Self-citationPatterns, correlates, and reduction of homework copying
Palazzo, D. J., Lee, Y.-J., Warnakulasooriya, R., & Pritchard, D. E. (2010) · Physical Review Special Topics - Physics Education Research · doi:10.1103/physrevstper.6.010104 · Cited by 102 · see citing works
Self-citationCarroll, J., Gottschall, J., Johnson, J. A., et al. (2012) · Palgrave Macmillan US eBooks · doi:10.1057/9781137002419_2 · Cited by 84 · see citing works
Immediate Feedback and Opportunity to Revise Answers
Attali, Y. (2010) · Applied Psychological Measurement · doi:10.1177/0146621610381755 · Cited by 38 · see citing works
Bringing student backgrounds online: MOOC user demographics, site usage, and online learning
DeBoer, J. J., Ho, A., Stump, G., et al. (2013) · Cited by 38 · see citing works
What course elements correlate with improvement on tests in introductory Newtonian mechanics?
Morote, E., & Pritchard, D. E. (2009) · American Journal of Physics · doi:10.1119/1.3139533 · Cited by 29 · see citing works
Self-citationModeling Applied to Problem Solving
Pawl, A., Barrantes, A., Pritchard, D. E., et al. (2009) · AIP conference proceedings · doi:10.1063/1.3266752 · Cited by 29 · see citing works
Self-citationWhen students can choose easy, medium, or hard homework problems
Teodorescu, R. E., Seaton, D. T., Cardamone, C. N., et al. (2012) · AIP conference proceedings · doi:10.1063/1.3679999 · Cited by 11 · see citing works
Self-citationRayyan, S., Pawl, A., Barrantes, A., et al. (2010) · AIP conference proceedings · doi:10.1063/1.3515221 · Cited by 5 · see citing works
Palazzo, D. J., Lee, Y., Warnakulasooriya, R., et al. (2010) · Physical Review Special Topics - Physics Education Research · doi:10.1103/physrevstper.6.029902 · Cited by 4 · see citing works
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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
Why do Institutions Offer MOOCs?
Hollands, F. M., & Tirthali, D. (2014) · Online Learning · doi:10.24059/olj.v18i3.464 · Cited by 101 · see citing works
Lan, A. S., Vats, D., Waters, A. E., & Baraniuk, R. G. (2015) · Proceedings of the Second (2015) ACM Conference on Learning @ Scale · doi:10.1145/2724660.2724664 · Cited by 60 · see citing works
- Authors
- Champaign, J., Colvin, K. F., Liu, A., Fredericks, C., Seaton, D., & Pritchard, D. E.
- Affiliations
- Massachusetts Institute of Technology (US)
- Venue
- Proceedings of the first ACM conference on Learning @ scale conference
- Publisher
- ACM
- Published
- 2014-03-04
- Pages
- 11-20
- OpenAlex
- W2032780221
- Cited by
- 50
- References
- 16
- Citations by year
- 2024: 1 · 2023: 3 · 2022: 2 · 2021: 2 · 2020: 3 · 2019: 6 · 2018: 5 · 2017: 7 · 2016: 8 · 2015: 8 · 2014: 5
- FWCI
- 5.92
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM Conference on Learning @ Scale (L@S) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Education › Online and Blended Learning
- Keywords
- MOOCs, time on task, student performance, skill development, item response theory, conceptual knowledge, study habits, edX
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