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“Learning latent engagement patterns of students in online courses,”
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Maintaining and cultivating student engagement is critical for learning. Understanding factors affecting student engagement will help in designing better courses and improving student retention. The large number of participants in massive open online courses (MOOCs) and data collected from their interaction with the MOOC open up avenues for studying student engagement at scale. In this work, we develop a framework for modeling and understanding student engagement in online courses based on student behavioral cues. Our first contribution is the abstraction of student engagement types using latent representations and using that in a probabilistic model to connect student behavior with course completion. We demonstrate that the latent formulation for engagement helps in predicting student survival across three MOOCs. Next, in order to initiate better instructor interventions, we need to be able to predict student survival early in the course. We demonstrate that we can predict student survival early in the course reliably using the latent model. Finally, we perform a closer quantitative analysis of user interaction with the MOOC and identify student activities that are good indicators for survival at different points in the course.
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“To this end, researchers have explored features such as students' prior experiences, motivation and home environment (DeBoer et al., 2013) and student engagement with the course (Aguiar et al., 2014; Ramesh et al., 2014).”
From Proactive and reactive engagement of artificial intelligence methods for education: a review (2023) · cites this paper as Ramesh et al., 2014
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Metadata
- Authors
- Ramesh, A., Goldwasser, D., Huang, B., Daume III, H., & Getoor, L.
- Affiliations
- University of Maryland, College Park (US)
- University of California, Santa Cruz (US)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2014-06-21
- Volume
- 28
- Issue
- 1
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2131148439
- Cited by
- 123
- References
- 13
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 6 · 2023: 6 · 2022: 9 · 2021: 8 · 2020: 17 · 2019: 13 · 2018: 16 · 2017: 11 · 2016: 12 · 2015: 11 · 2014: 5
- FWCI
- 8.25
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- Funders
- National Science Foundation (0937094, 0746930)
- 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
- student engagement, online courses, massive open online courses, course completion, instructor intervention, user interaction analysis
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.
What counts as a claim ›How we measure confidence ›How we check this ›
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