“Predicting MOOC dropout over weeks using machine learning methods,”
Open accessAbstract
With high dropout rates as observed in many current larger-scale online courses, mechanisms that are able to predict student dropout become increasingly important.While this problem is partially solved for students that are active in online forums, this is not yet the case for the more general student population.In this paper, we present an approach that works on click-stream data.Among other features, the machine learning algorithm takes the weekly history of student data into account and thus is able to notice changes in student behavior over time.In the later phases of a course (i.e., once such history data is available), this approach is able to predict dropout significantly better than baseline methods.
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Using historical click-stream data in conjunction with present click-stream data allowed Kloft et al. (2014) to effectively predict drop-outs weekly using a simple Support Vector Machine algorithm.
“Using historical click-stream data in conjuction with present click-stream data, allowed (Kloft et al., 2014) to effectively predict drop-outs weekly using a simple Support Vector Machine algorithm.”
From Proactive and reactive engagement of artificial intelligence methods for education: a review (2023) · cites this paper as Kloft et al., 2014
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LIBLINEAR: A Library for Large Linear Classification
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lp-Norm Multiple Kernel Learning
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Shared Task on Prediction of Dropout Over Time in Massively Open Online Courses
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Prediction of students’ early dropout based on their interaction logs in online learning environment
Mubarak, A. A., Cao, H., & Zhang, W. (2022) · Interactive Learning Environments · doi:10.1080/10494820.2020.1727529 · Cited by 170 · see citing works
Proactive and reactive engagement of artificial intelligence methods for education: a review
Mallik, S., & Gangopadhyay, A. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1151391 · Cited by 139 · see citing works
Coussement, K., Phan, M., De Caigny, A., Benoit, D. F., & Raes, A. (2020) · Decision Support Systems · doi:10.1016/j.dss.2020.113325 · Cited by 125 · see citing works
How Widely Can Prediction Models Be Generalized? Performance Prediction in Blended Courses
Gitinabard, N., Xu, Y., Heckman, S., Barnes, T., & Lynch, C. F. (2019) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2019.2911832 · Cited by 60 · see citing works
MOOCs Meet Measurement Theory: A Topic-Modelling Approach
He, J., Rubinstein, B., Bailey, J., Zhang, R., Milligan, S., & Chan, J. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10151 · Cited by 10 · see citing works
- Authors
- Kloft, M., Stiehler, F., Zheng, Z., & Pinkwart, N.
- Affiliations
- Humboldt-Universität zu Berlin (DE)
- Venue
- Proceedings of the EMNLP 2014 Workshop on Analysis of Large Scale Social Interaction in MOOCs
- Published
- 2014
- Pages
- 60-65
- OpenAlex
- W2250880511
- Cited by
- 325
- References
- 7
- Citations by year
- 2026: 14 · 2025: 12 · 2024: 17 · 2023: 32 · 2022: 34 · 2021: 37 · 2020: 39 · 2019: 42 · 2018: 34 · 2017: 32 · 2016: 16 · 2015: 14 · 2014: 2
- FWCI
- 13.54
- Type
- conference paper
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Education › Online and Blended Learning
- Social Sciences › Demography › Evasion and Academic Success Factors
- Keywords
- MOOC dropout prediction, school dropout, clickstream data, online learning, student behavior, longitudinal student data, course completion
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