The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
“Learning latent engagement patterns of students in online courses,”
Open accessAbstract
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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Citations
Cites
Student Engagement and Student Learning: Testing the Linkages*
Carini, R. M., Kuh, G. D., & Klein, S. P. (2006) · Research in Higher Education · doi:10.1007/s11162-005-8150-9 · Cited by 1,799
Kuh, G. D. (2003) · Change The Magazine of Higher Learning · doi:10.1080/00091380309604090 · Cited by 1,709
User Models for Adaptive Hypermedia and Adaptive Educational Systems
Brusilovsky, P. L., & Millán, E. (2007) · Springer eBooks · doi:10.1007/978-3-540-72079-9_1 · Cited by 1,089
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
MOOCs and the funnel of participation
Clow, D. (2013) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/2460296.2460332 · Cited by 571
Engaging with massive online courses
Anderson, A., Huttenlocher, D. P., Kleinberg, J. M., & Leskovec, J. (2014) · International World Wide Web Conference (WWW) · doi:10.1145/2566486.2568042 · Cited by 559
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Getoor, L., & Taskar, B. (2007) · The MIT Press eBooks · Cited by 502
Self-citationWilson, T., Hoffmann, P., Somasundaran, S., Kessler, J. S., Wiebe, J., Choi, Y., Cardie, C., Riloff, E., & Patwardhan, S. (2005) · doi:10.3115/1225733.1225751 · Cited by 469
Preventing Student Dropout in Distance Learning Using Machine Learning Techniques
Kotsiantis, S. B., Pierrakeas, C. J., & Pintelas, P. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-45226-3_37 · Cited by 228
Predicting Student Retention in Massive Open Online Courses using Hidden Markov Models
Balakrishnan, G. (2013) · UC Berkeley · Cited by 156
Flagged first, then by citations
Research works printed but not linked: 1
- Yang, D.; Sinha, T.; Adamson, D.; and Penstein, R. 2013. Turn on, tune in, drop out: Anticipating student dropouts in massive open online courses. In NIPS Workshop on Data Driven Education .
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Items 101–123 of 123
Zeng, Z., Chaturvedi, S., Bhat, S., & Roth, D. (2019) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3303772.3303810 · Cited by 1
Yan, N., Lin, T., Zheng, Y., & Xu, Z. (2021) · IEEE International Conference on Teaching, Assessment, and Learning for Engineering · doi:10.1109/tale52509.2021.9678819 · Cited by 1
Assessing heterogeneity in MOOC student performance through composite-based path modelling
Davino, C., Lamberti, G., & Vistocco, D. (2023) · Quality & Quantity · doi:10.1007/s11135-023-01760-2 · Cited by 1
Sequential engagement-based online learning analytics and prediction
Song, X., & Li, J. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-823410-5.00012-7 · Cited by 1
Shirazi, S. H. A., Pang, A., Knight, A., & Salloum, M. (2025) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3641554.3701881 · Cited by 1
Predicting Off-task Behaviors for Adaptive Vocabulary Learning System.
Nam, S. (2016) · Educational Data Mining · Cited by 1
Kokoç, M., Ilgaz, H., & Altun, A. (2021) · IGI Global eBooks · doi:10.4018/978-1-7998-8047-9.ch072 · Cited by 1
Fife, M.-A. (2016) · Open University of Cape Town (University of Cape Town) · Cited by 1
A Probabilistic Approach to Modeling Socio-Behavioral Interactions
Ramesh, A. (2016) · University Libraries (University of Maryland) · doi:10.13016/m2cv6z · Cited by 1
Self-citationAn analysis of learners' learning behavior in Massive Open Online
Qin, J., Jia, Z., & Li, H. (2017) · Conference on Education and Teaching in Colleges and Universities (CETCU) · doi:10.2991/cetcu-17.2017.26
An In-Depth Survey on Massive Open Online Course Outcome Prediction
Gaurav, G., Kaur, G., & Agrawal, P. (2020) · Algorithms for intelligent systems · doi:10.1007/978-981-15-1632-0_2
An Unsupervised Approach to User Characterization in Online Learning and Social Platforms
Vilenchik, D. (2020) · Mathematics in mind · doi:10.1007/978-3-030-59177-9_2
Timely dropout prediction with learner behavior data
Liu, K. (2022) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/155739
Work in Progress: Adapting eduScrum to the Online Teaching Format
Andersson, C., & Kroisandt, G. (2025) · IEEE World Engineering Education Conference (EDUNINE) · doi:10.1109/edunine62377.2025.10981396
Kim, T.-H., Liu, Z., Xing, W., Li, H., & Oh, H.-J. (2025) · Proceedings. · doi:10.22318/icls2025.183564
Hierarchical BERT for Emotion Detection and Engagement Anomaly Analysis in Online Learning Forums
Abd-Elmegid, L. A., & Shamaki, A.-A. M. (2025) · IEEE International Conference on Communication, Networks and Satellite (COMNETSAT) · doi:10.1109/comnetsat68601.2025.11324927
The many faces of adult learners
OECD (2026) · doi:10.1787/a5a1893a-en
Temporal Bipartite Graph Representation Learning for Behavior Anomaly Detection
Kong, Y., Xu, Y., Wen, D., Zhang, Y., Li, B., & Zhang, W. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-2494-4_21
Predictive and Interpretable Deep Learning Models for Student Dropout in MOOCs
Dhaoui, F., Rezgui, K., & Azzouna, N. B. (2026) · Intelligence in Business and Industry (IBI) · doi:10.1109/ibi68858.2026.11604094
Ouassil, M. A., Jebbari, M., Errami, M., Rachidi, R., Hamida, S., Cherradi, B., & Raihani, A. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-02480-w
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
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