“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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Researchers have explored features such as students' prior experiences, motivation and home environment, and student engagement with the course for attrition prediction.
“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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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 · see citing works
Kuh, G. D. (2003) · Change The Magazine of Higher Learning · doi:10.1080/00091380309604090 · Cited by 1,600 · see citing works
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 994 · see citing works
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 · see citing works
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 · see citing works
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 524 · see citing works
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Getoor, L., & Taskar, B. (2007) · The MIT Press eBooks · Cited by 502 · see citing works
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 · see citing works
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 · see citing works
Predicting Student Retention in Massive Open Online Courses using Hidden Markov Models
Balakrishnan, G. (2013) · UC Berkeley · Cited by 154 · see citing works
Probabilistic Similarity LogicPreprint
Broecheler, M., Mihalkova, L. S., & Getoor, L. C. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1203.3469 · Cited by 143 · see citing works
Self-citationHinge-loss Markov Random Fields: Convex Inference for Structured PredictionPreprint
Bach, S. H., Huang, B., London, B., & Getoor, L. C. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1309.6813 · Cited by 83 · see citing works
Self-citationA handbook of parametric survival models for actuarial use
Richards, S. J. (2010) · Scandinavian Actuarial Journal · doi:10.1080/03461238.2010.506688 · Cited by 53 · see citing works
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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 .
Cited by
Sorted by citations
Temporal Models for Predicting Student Dropout in Massive Open Online Courses
Mi, F., & Yeung, D. (2015) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2015.174 · Cited by 287 · see citing works
Bulathwela, S., Pérez-Ortiz, M., Holloway, C., Cukurova, M., & Shawe-Taylor, J. (2024) · Sustainability · doi:10.3390/su16020781 · Cited by 269 · see citing works
Understanding Dropouts in MOOCs
Feng, W., Tang, J., & Liu, T. X. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.3301517 · Cited by 249 · see citing works
Identifying At-Risk Students in Massive Open Online Courses
He, J., Bailey, J., Rubinstein, B. I. P., & Zhang, R. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9471 · Cited by 215 · see citing works
Hinge-Loss Markov Random Fields and Probabilistic Soft LogicPreprint
Bach, S. H., Broecheler, M., Huang, B., & Getoor, L. C. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.04406 · Cited by 185 · see citing works
Self-citationHow 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
Prediction in MOOCs: A Review and Future Research Directions
Moreno-Marcos, P. M., Alario‐Hoyos, C., Muñoz‐Merino, P. J., & Kloos, C. D. (2018) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2018.2856808 · Cited by 180 · see citing works
Modeling and Predicting Learning Behavior in MOOCs
Qiu, J., Tang, J., Liu, T. X., Gong, J., Zhang, C., Zhang, Q., & Xue, Y. (2016) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/2835776.2835842 · Cited by 177 · see citing works
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
Likely to stop? Predicting Stopout in Massive Open Online CoursesPreprint
Taylor, C., Veeramachaneni, K. K., & O’Reilly, U.-M. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.3382 · Cited by 142 · see citing works
How Learners’ Interactions Sustain Engagement: A MOOC Case Study
Sunar, A. S., White, S., Abdullah, N. A., & Davis, H. (2016) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2016.2633268 · Cited by 141 · 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
Machine learning approaches to predict learning outcomes in Massive open online courses
Al-Shabandar, R., Hussain, A. J., Laws, A., Keight, R., Lunn, J., & Radi, N. (2017) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7965922 · Cited by 119 · see citing works
Zhang, Z., Li, Z., Liu, H., Cao, T., & Liu, S. (2019) · Journal of Educational Computing Research · doi:10.1177/0735633119825575 · Cited by 117 · see citing works
Predicting student performance in a blended MOOC
Conijn, R., van den Beemt, A., & Cuijpers, P. P. (2018) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12270 · Cited by 115 · see citing works
Collective Spammer Detection in Evolving Multi-Relational Social Networks
Fakhraei, S., Foulds, J. R., Shashanka, M. V. S., & Getoor, L. C. (2015) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2783258.2788606 · Cited by 112 · see citing works
Self-citationLi, Q., & Baker, R. B. (2018) · Computers & Education · doi:10.1016/j.compedu.2018.08.005 · Cited by 111 · see citing works
Artificial Intelligence for Social Good: A SurveyPreprint
Shi, Z. R., Wang, C., & Fang, F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.01818 · Cited by 108 · see citing works
Understanding MOOC Discussion Forums using Seeded LDA
Ramesh, A., Goldwasser, D., Huang, B., Daumé, H., & Getoor, L. C. (2014) · Workshop on Innovative Use of NLP for Building Educational Applications · doi:10.3115/v1/w14-1804 · Cited by 95 · see citing works
Self-citationInvestigating learners' behaviors and discourse content in MOOC course reviews
Peng, X., & Xu, Q. (2019) · Computers & Education · doi:10.1016/j.compedu.2019.103673 · Cited by 93 · see citing works
Types of Participant Behavior in a Massive Open Online Course
Kahan, T., Soffer, T., & Nachmias, R. (2017) · The International Review of Research in Open and Distributed Learning · doi:10.19173/irrodl.v18i6.3087 · Cited by 91 · see citing works
Jing, X., & Tang, J. (2017) · IEEE/WIC/ACM International Conference on Web Intelligence · doi:10.1145/3106426.3106478 · Cited by 90 · see citing works
MOOC Dropout Prediction Using a Hybrid Algorithm Based on Decision Tree and Extreme Learning Machine
Chen, J., Feng, J. H., Sun, X., Wu, N., Yang, Z., & Chen, S. (2019) · Mathematical Problems in Engineering · doi:10.1155/2019/8404653 · Cited by 90 · see citing works
MOOC student dropout prediction model based on learning behavior features and parameter optimization
Jin, C. (2020) · Interactive Learning Environments · doi:10.1080/10494820.2020.1802300 · Cited by 90 · see citing works
Student dropout prediction in massive open online courses by convolutional neural networks
Qiu, L., Liu, Y., Hu, Q., & Liu, Y. (2018) · Soft Computing · doi:10.1007/s00500-018-3581-3 · Cited by 86 · see citing works
Sinha, T., Li, N., Jermann, P., & Dillenbourg, P. (2014) · doi:10.3115/v1/w14-4108 · Cited by 74 · see citing works
Consideration of the local correlation of learning behaviors to predict dropouts from MOOCs
Wen, Y., Tian, Y., Wen, B., Zhou, Q., Cai, G., & Liu, S. (2019) · Tsinghua Science & Technology · doi:10.26599/tst.2019.9010013 · Cited by 67 · see citing works
Weakly Supervised Models of Aspect-Sentiment for Online Course Discussion Forums
Ramesh, A., Kumar, S. H., Foulds, J. R., & Getoor, L. C. (2015) · doi:10.3115/v1/p15-1008 · Cited by 66 · see citing works
Self-citationCounting Clicks is Not Enough: Validating a Theorized Model of Engagement in Learning Analytics
Fincham, E., Whitelock-Wainwright, A., Kovanović, V., Joksimović, S., van Staalduinen, J.-P., & Gašević, D. (2019) · Proceedings of the 9th International Conference on Learning Analytics & Knowledge · doi:10.1145/3303772.3303775 · Cited by 57 · see citing works
SEPN: A Sequential Engagement Based Academic Performance Prediction Model
Song, X., Li, J., Sun, S., Yin, H., Dawson, P., & Doss, R. (2020) · IEEE Intelligent Systems · doi:10.1109/mis.2020.3006961 · Cited by 54 · see citing works
Learning Instructor Intervention from MOOC Forums: Early Results and IssuesPreprint
Chandrasekaran, M. K., Kan, M., Tan, B. C. Y., & Ragupathi, K. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1504.07206 · Cited by 51 · see citing works
Ahmadi, G., Mohammadi, A., Asadzandi, S., Shah, M., & Mojtahedzadeh, R. (2023) · The International Review of Research in Open and Distributed Learning · doi:10.19173/irrodl.v24i1.6453 · Cited by 45 · see citing works
Wu, N., Zhang, L., Gao, Y. D., Zhang, M., Sun, X., & Feng, J. H. (2019) · ACM Turing Celebration Conference - China (ACM TURC) · doi:10.1145/3321408.3322848 · Cited by 45 · see citing works
Nguyen, A., Gardner, L. A., & Sheridan, D. (2018) · British Journal of Educational Technology · doi:10.1111/bjet.12625 · Cited by 43 · see citing works
Goh, T. (2025) · Journal of Computers in Education · doi:10.1007/s40692-025-00358-x · Cited by 41 · see citing works
Towards Feature Engineering at Scale for Data from Massive Open Online CoursesPreprint
Veeramachaneni, K. K., O’Reilly, U.-M., & Taylor, C. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1407.5238 · Cited by 39 · see citing works
Reimagining the machine learning life cycle to improve educational outcomes of students
Liu, L. T., Wang, S., Britton, T., & Abebe, R. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2204781120 · Cited by 36 · see citing works
A systematic review for MOOC dropout prediction from the perspective of machine learning
Jing, C., Fang, B., Zhang, H., & Xue, X. (2022) · Interactive Learning Environments · doi:10.1080/10494820.2022.2124425 · Cited by 36 · see citing works
Analysing the predictive power for anticipating assignment grades in a massive open online course
Moreno-Marcos, P. M., Muñoz‐Merino, P. J., Alario‐Hoyos, C., Ayres, I. E., & Kloos, C. D. (2018) · Behaviour and Information Technology · doi:10.1080/0144929x.2018.1458904 · Cited by 35 · see citing works
A nonlinear state space model for identifying at-risk students in open online courses
Wang, F., & Chen, L. (2016) · Educational Data Mining · Cited by 32 · see citing works
Online Academic Course Performance Prediction Using Relational Graph Convolutional Neural Network.
Karimi, H. R., Derr, T., Huang, J., & Tang, J. (2020) · Educational Data Mining · Cited by 32 · see citing works
Educational Data Mining: Dropout Prediction in XuetangX MOOCs
Xu, C., Zhu, G., Ye, J., & Shu, J. (2022) · Neural Processing Letters · doi:10.1007/s11063-022-10745-5 · Cited by 31 · 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
Refocusing the lens on engagement in MOOCs
Crues, R. W., Bosch, N., Perry, M., Angrave, L., Shaik, N., & Bhat, S. (2018) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/3231644.3231658 · Cited by 24 · see citing works
Predicting Engagement in Video LecturesPreprint
Bulathwela, S., Pérez‐Ortiz, M., Lipani, A., Yılmaz, E., & Shawe-Taylor, J. S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.00592 · Cited by 24 · see citing works
Prediction of Prospective User Engagement with Intelligent Assistants
Sano, S., Kaji, N., & Sassano, M. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1114 · Cited by 23 · see citing works
Modeling Engagement in Self-Directed Learning Systems Using Principal Component Analysis
Hershcovits, H., Vilenchik, D., & Gal, K. (2019) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2019.2922902 · Cited by 23 · see citing works
Modelling and predicting User Engagement in mobile applications
Bárbaro, E., Grua, E. M., Malavolta, I., Stercevic, M., Weusthof, E., & van den Hoven, J. (2019) · Data Science · doi:10.3233/ds-190027 · Cited by 22 · see citing works
Student engagement in massive open online courses
Sinclair, J. E., & Kalvala, S. (2016) · International Journal of Learning Technology · doi:10.1504/ijlt.2016.079035 · Cited by 21 · see citing works
Understanding Learner’s Drop-Out in MOOCs
Itani, A., Brisson, L., & Serge Garlatti, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-03493-1_25 · Cited by 21 · see citing works
Mutual Reinforcement of Academic Performance Prediction and Library Book Recommendation
Lian, D., Ye, Y., Zhu, W., Liu, Q., Xie, X., & Xiong, H. (2016) · Proceedings · doi:10.1109/icdm.2016.0130 · Cited by 20 · see citing works
Liu, Z., Zhang, W., Cheng, H. N. H., Sun, J., & Liu, S. (2018) · International Journal of Distance Education Technologies · doi:10.4018/ijdet.2018040103 · Cited by 20 · see citing works
Predicting dropout in online learning environments
Radovanović, S., Delibašić, B., & Suknović, M. (2020) · Computer Science and Information Systems · doi:10.2298/csis200920053r · Cited by 18 · see citing works
Modeling the Effort and Learning Ability of Students in MOOCs
Gao, L., Zhao, Z., Qi, L., Liang, Y., & Du, J. (2019) · IEEE Access · doi:10.1109/access.2019.2937985 · Cited by 16 · see citing works
Modeling Student Learning Styles in MOOCs
Shi, Y., Peng, Z., & Wang, H. (2017) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3132847.3132965 · Cited by 15 · see citing works
Integrating Students’ Behavioral Signals and Academic Profiles in Early Warning System
Nam, S., & Samson, P. J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-23204-7_29 · Cited by 15 · see citing works
Uncovering Engagement Profiles of Young Learners in K–8 Education through Learning Analytics
Er, E., Silik, S., & Cansiz, S. (2024) · Journal of Learning Analytics · doi:10.18608/jla.2024.8133 · Cited by 14 · see citing works
Bulathwela, S., Pérez‐Ortiz, M., Yılmaz, E., & Shawe-Taylor, J. S. (2022) · Sustainability · doi:10.3390/su141811682 · Cited by 14 · see citing works
The Promise of MOOCs Revisited? Demographics of Learners Preparing for University
Meaney, M. J., & Fikes, T. G. (2023) · Journal of Learning Analytics · doi:10.18608/jla.2023.7807 · Cited by 14 · see citing works
Identifying At-Risk K-12 Students in Multimodal Online Environments: A Machine Learning ApproachPreprint
Li, H., Ding, W., & Liu, Z. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.09670 · Cited by 14 · see citing works
Xing, W., Li, H., Kim, T.-H., Zhu, W., & Song, Y. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-13038-7 · Cited by 13 · see citing works
Kang, I. G. (2020) · The International Review of Research in Open and Distributed Learning · doi:10.19173/irrodl.v21i4.4645 · Cited by 13 · see citing works
Massive Open Online Courses Temporal Profiling for Dropout Prediction
Hagedoorn, T. R., & Spanakis, G. (2017) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2017.00045 · Cited by 13 · see citing works
A Real-Time Predictive Model for Identifying Course Dropout in Online Higher Education
Bañeres, D., Rodríguez, M. E., & Guerrero‐Roldán, A. (2023) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2023.3267275 · Cited by 12 · see citing works
Modeling Consistency Using Engagement Patterns in Online Courses
Zhou, J., & Bhat, S. (2021) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3448139.3448161 · Cited by 12 · see citing works
Interpretable Engagement Models for MOOCs Using Hinge-Loss Markov Random Fields
Ramesh, A., Goldwasser, D., Huang, B., Daumé, H., & Getoor, L. C. (2018) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2018.2889953 · Cited by 12 · see citing works
Self-citationLiu, K., Tatinati, S., & Khong, A. W. H. (2020) · IEEE International Conference on Teaching, Assessment, and Learning for Engineering · doi:10.1109/tale48869.2020.9368317 · Cited by 12 · see citing works
Decoding Engagement in MOOCs: An Indian Learner Perspective
Kaveri, A., Gunasekar, S., Gupta, D., & Pratap, M. (2016) · IEEE International Conference on Technology for Education (T4E) · doi:10.1109/t4e.2016.027 · Cited by 12 · see citing works
Evolutionary multi-objective optimization for multi-view clustering
Jiang, B., Qiu, F., Yang, S., & Wang, L. (2016) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2016.7744208 · Cited by 11 · see citing works
ONet – A Temporal Meta Embedding Network for MOOC Dropout Prediction
Pulikottil, S. C., & Gupta, M. (2020) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata50022.2020.9378001 · Cited by 11 · see citing works
PEEK: A Large Dataset of Learner Engagement with Educational VideosPreprint
Bulathwela, S., Pérez‐Ortiz, M., Novak, E., Yılmaz, E., & Shawe-Taylor, J. S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.03154 · Cited by 11 · 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
Click-Based Representation Learning Framework of Student Navigational Behavior in MOOCs
Amoudi, S. A., Alhothali, A., Mirza, R., Assalahi, H. M., & Aldosemani, T. I. (2024) · IEEE Access · doi:10.1109/access.2024.3450514 · Cited by 10 · see citing works
Exploring causes for the dropout on massive open online courses
Niu, Z., Li, W., Yan, X., & Wu, N. (2018) · ACM Turing Celebration Conference - China (ACM TURC) · doi:10.1145/3210713.3210727 · Cited by 10 · see citing works
Participating by activity or by week in MOOCs
Baikadi, A., Epp, C. D., & Schunn, C. D. (2018) · Information and Learning Sciences · doi:10.1108/ils-04-2018-0033 · Cited by 10 · see citing works
MOOCs Recommender System with Siamese Neural Network
Faroughi, A., & Moradi, P. (2022) · International and National Conference on E-Learning and E-Teaching (ICeLeT) · doi:10.1109/icelet55619.2022.9765439 · Cited by 9 · see citing works
Paired-dual learning for fast training of latent variable hinge-loss mrfs
Bach, S. H. (2015) · Cited by 9 · see citing works
VLEngagement: A Dataset of Scientific Video Lectures for Evaluating Population-based EngagementPreprint
Bulathwela, S., Pérez‐Ortiz, M., Yılmaz, E., & Shawe-Taylor, J. S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.02273 · Cited by 9 · see citing works
Predicting Students Drop-out in MOOC from Learning Behavior Using Machine Learning
Alj, Z., & Malki, M. O. C. (2022) · Journal of Uncertain Systems · doi:10.1142/s1752890922500118 · Cited by 8 · see citing works
Engagement Measures in Massive Open Online Courses
Sinclair, J. E., & Kalvala, S. (2015) · Communications in computer and information science · doi:10.1007/978-3-319-22629-3_1 · Cited by 8 · see citing works
The Challenges of Massive Open Online Courses (MOOCs)
Pike, G. E., & Gore, H. (2018) · Springer eBooks · doi:10.1007/978-3-319-78298-0_8 · Cited by 8 · see citing works
Lian, D., & Liu, Q. (2018) · Journal of Computer Science and Technology · doi:10.1007/s11390-018-1847-y · Cited by 8 · see citing works
Motivating Learners in Massive Open Online Courses: A Design-Based Research Approach.
Li, K. (2015) · OhioLink ETD Center (Ohio Library and Information Network) · Cited by 7 · see citing works
TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resources
Bulathwela, S., Pérez‐Ortiz, M., Yılmaz, E., & Shawe-Taylor, J. S. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i01.5395 · Cited by 6 · see citing works
When and Who at Risk? Call Back at These Critical Points.
Li, Y., Fu, C., & Zhang, Y. (2017) · Educational Data Mining · Cited by 6 · see citing works
A Hybrid Deep Learning Model for MOOCs Dropout Prediction
Liu, H., & Zhang, W. (2022) · International Conference on Computer Science and Technologies in Education (CSTE) · doi:10.1109/cste55932.2022.00039 · Cited by 6 · see citing works
Tam, G., Chan, E. Y. Y., & Liu, S. (2018) · JMIR Medical Education · doi:10.2196/mededu.8495 · Cited by 6 · see citing works
Kim, H., Park, G., & Cho, M. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12782-0 · Cited by 5 · see citing works
Content analysis of MOOC forums: The characteristics of the learners' discourse in forums
Cohen, A., Shimony, U., & Nachmias, R. (2017) · Intelligent Systems Conference (IntelliSys) · doi:10.1109/intellisys.2017.8324235 · Cited by 5 · see citing works
Budgeted online collective inference
Pujara, J., London, B., & Getoor, L. C. (2015) · Uncertainty in Artificial Intelligence · Cited by 5 · see citing works
Self-citationELBA: Exceptional Learning Behavior Analysis.
Du, X., Duivesteijn, W., Klabbers, M., & Pechenizkiy, M. (2018) · TU/e Research Portal · Cited by 5 · see citing works
Havas, S., Imanian, N., & Moradi, P. (2022) · International and National Conference on E-Learning and E-Teaching (ICeLeT) · doi:10.1109/icelet55619.2022.9765436 · Cited by 4 · see citing works
Kokoç, M., Ilgaz, H., & Altun, A. (2019) · Advances in educational technologies and instructional design book series · doi:10.4018/978-1-7998-0119-1.ch005 · Cited by 3 · see citing works
Predicting Student Examinee Rate in Massive Open Online Courses
Lu, W., Wang, T., Jiao, M., Zhang, X., Wang, S., Du, X., & Chen, H. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-55705-2_27 · Cited by 3 · see citing works
Enhancing Student Dropout Prediction in MOOCs Using Graph Neural Networks and Link Augmentation
Khoushehgir, F., Noshad, Z., Mafakheri, A., & Sulaimany, S. (2025) · SN Computer Science · doi:10.1007/s42979-025-04560-7 · Cited by 2 · see citing works
Proposing Big Data Architecture for Addressing Dropout Problem in MOOC Platforms
Nguyen, A.-K., Nguyen, D.-K., Vo, K. T., Nguyen, T., Nguyen-Hoang, T.-A., Dinh, N.-T., & Nguyen, T. (2024) · International Conference on Advanced Technologies for Communications · doi:10.1109/atc63255.2024.10908166 · Cited by 2 · see citing works
Learning from Learning Analytics: How Much Do We Know About Patterns of Student Engagement?
Chiera, B., Korolkiewicz, M. W., & Schultz, L. (2021) · Policy implications of research in education · doi:10.1007/978-3-030-76841-6_8 · Cited by 2 · see citing works
Effective Forum Curation via Multi-task Learning.
Brahman, F., Varghese, N., Bhat, S., & Chaturvedi, S. (2020) · Educational Data Mining · Cited by 2 · see citing works
- 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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