Prediction of students’ early dropout based on their interaction logs in online learning environment
Abstract
Online learning has become more popular in higher education since it adds convenience and flexibility to students’ schedule. But, it has faced difficulties in the retention of the continuity of students and ensure continual growth in course. Dropout is a concerning factor in online course continuity. Therefore, it has sparked great interest among educators and researchers to offer many models and strategies to reduce online course students’ dropout by analyzing students’ behavior and their individual and academic information. However, online education platforms still face challenges of high dropout rate and the difficulty of accurate prediction to reduce it. The key aim of the present study is to construct a predictive model to early predict students who are at-risk of dropout. This model is useful to the course instructors to make effective and timely interventions. We have noticed that dropout prediction is basically a sequence labeling or time series prediction problem. For these reasons, we proposed two models; the Logistic Regression by adding a regularization term and the Input-Output Hidden Markov Model (IOHMM). Results showed that the proposed models achieved an accuracy of 84% compared to the baseline of Machine Learning Models for prediction of the students at-risk of dropping out.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Other EWS deployments have detected an increase of learners' success, i.e., better performance and dropout reduction.
“Other authors that have deployed EWS in real learning environments have also detected an increase of learners' success, i.e., a better learners' performance and dropout reduction.”
From Experiences in the use of an adaptive intelligent system to enhance online learners' performance: a case study in Economics and Business courses (2021) · cites this paper as Mubarak et al. 2020
Cites
The meaning and use of the area under a receiver operating characteristic (ROC) curve.
Hanley, J. A., & McNeil, B. J. (1982) · Radiology · doi:10.1148/radiology.143.1.7063747 · Cited by 21,856 · see citing works
Advances in neural information processing systems 7
(1996) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(96)90463-0 · Cited by 14,759 · see citing works
Top 10 algorithms in data mining
Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G. J., Ng, A., Liu, B., Yu, P. S., et al. (2008) · Knowledge and Information Systems · doi:10.1007/s10115-007-0114-2 · Cited by 5,667 · see citing works
Journal of Universal Computer Science
Salomaa, A. K., Calude, C. S., & Maurer, H. A. (2020) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.3217/jucs-000-00-0109 · Cited by 3,394 · see citing works
The expectation-maximization algorithm
Moon, T. K. (1996) · IEEE Signal Processing Magazine · doi:10.1109/79.543975 · Cited by 3,197 · see citing works
Supervised Sequence Labelling with Recurrent Neural Networks
Graves, A. (2012) · Studies in Computational Intelligence · doi:10.1007/978-3-642-24797-2 · Cited by 3,120 · see citing works
Educational Data Mining: A review of the state of the art
Romero, C., & Ventura, S. (2010) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2010.2053532 · Cited by 2,251 · see citing works
Mining data with random forests: A survey and results of new tests
Verikas, A., Gelžinis, A., & Bačauskienė, M. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.08.011 · Cited by 728 · see citing works
Reynolds, C. R. (2008) · Encyclopedia of Special Education · doi:10.1002/9780470373699.speced0485 · Cited by 618 · see citing works
From time series to linear system—Part I. Finite dimensional linear time invariant systems
Willems, J. C. (1986) · Automatica · doi:10.1016/0005-1098(86)90066-x · Cited by 570 · see citing works
An overview of learning analytics
Clow, D. (2013) · Teaching in Higher Education · doi:10.1080/13562517.2013.827653 · Cited by 436 · see citing works
Early Alert of Academically At-Risk Students: An Open Source Analytics Initiative
Jayaprakash, S. M., Moody, E. W., Lauría, E. J. M., Regan, J. R., & Baron, J. D. (2014) · Journal of Learning Analytics · doi:10.18608/jla.2014.11.3 · Cited by 424 · see citing works
Dropout prediction in e-learning courses through the combination of machine learning techniques
Lykourentzou, I., Giannoukos, I., Nikolopoulos, V., Mpardis, G., & Loumos, V. G. (2009) · Computers & Education · doi:10.1016/j.compedu.2009.05.010 · Cited by 401 · see citing works
Identifying significant indicators using LMS data to predict course achievement in online learning
You, J. W. (2016) · The Internet and Higher Education · doi:10.1016/j.iheduc.2015.11.003 · Cited by 388 · see citing works
Models for early prediction of at-risk students in a course using standards-based grading
Marbouti, F., Diefes‐Dux, H. A., & Madhavan, K. P. C. (2016) · Computers & Education · doi:10.1016/j.compedu.2016.09.005 · Cited by 364 · see citing works
“Predicting MOOC dropout over weeks using machine learning methods,”
Kloft, M., Stiehler, F., Zheng, Z., & Pinkwart, N. (2014) · Proceedings of the EMNLP 2014 Workshop on Analysis of Large Scale Social Interaction in MOOCs · doi:10.3115/v1/w14-4111 · Cited by 325 · see citing works
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
Applying Data Mining Techniques to e-Learning Problems
Castro, F., Vellido, A., Àngela Nebot, B., & Mugica, F. (2007) · Studies in computational intelligence · doi:10.1007/978-3-540-71974-8_8 · Cited by 280 · see citing works
Zhang, G., Anderson, T. J., Ohland, M., & Thorndyke, B. (2004) · Journal of Engineering Education · doi:10.1002/j.2168-9830.2004.tb00820.x · Cited by 266 · see citing works
Advanced data mining techniques
(2008) · Choice Reviews Online · doi:10.5860/choice.45-6838 · Cited by 236 · see citing works
Supporting Students in Online, Open and Distance Learning
Simpson, O. (2018) · Routledge eBooks · doi:10.4324/9780203417003 · Cited by 230 · 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
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
Lu, O. H. T., Huang, J. C. H., Huang, A. Y. Q., & Yang, S. J. H. (2017) · Interactive Learning Environments · doi:10.1080/10494820.2016.1278391 · Cited by 167 · see citing works
Prediction of Student Dropout in E-Learning Program Through the Use of Machine Learning Method
Tan, M., & Shao, P. (2015) · International Journal of Emerging Technologies in Learning (iJET) · doi:10.3991/ijet.v10i1.4189 · Cited by 163 · see citing works
Big Data and Data Analytics Research
Lytras, M. D., Raghavan, V. V., & Damiani, E. (2016) · International Journal on Semantic Web and Information Systems · doi:10.4018/ijswis.2017010101 · Cited by 156 · see citing works
Lonn, S., Aguilar, S. J., & Teasley, S. D. (2015) · Computers in Human Behavior · doi:10.1016/j.chb.2014.07.013 · Cited by 152 · 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
Monitoring student progress using virtual appliances: A case study
Romero-Zaldivar, V.-A., Pardo, A., Burgos, D., & Kloos, C. D. (2011) · Computers & Education · doi:10.1016/j.compedu.2011.12.003 · Cited by 138 · see citing works
An analysis of multiple factors affecting retention in Web-based community college courses
Doherty, W. (2006) · The Internet and Higher Education · doi:10.1016/j.iheduc.2006.08.004 · Cited by 124 · see citing works
“Learning latent engagement patterns of students in online courses,”
Ramesh, A., Goldwasser, D., Huang, B., Daume III, H., & Getoor, L. (2014) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v28i1.8920 · Cited by 123 · see citing works
Data for free: Using LMS activity logs to measure community in online courses
Black, E. W., Dawson, K., & Priem, J. R. (2008) · The Internet and Higher Education · doi:10.1016/j.iheduc.2008.03.002 · Cited by 118 · see citing works
A Robust Machine Learning Technique to Predict Low-performing Students
Liao, S. N., Zingaro, D., Thai, K., Alvarado, C., Griswold, W. G., & Porter, L. (2019) · ACM Transactions on Computing Education · doi:10.1145/3277569 · Cited by 91 · see citing works
Barbosa, J. L. V., Rigo, S. J., & Cambruzzi, W. (2020) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.3217/jucs-021-01-0023 · Cited by 78 · see citing works
Machine literature searching X. Machine language; factors underlying its design and development
Perry, J. W., Kent, A., & Berry, M. M. (1955) · American Documentation · doi:10.1002/asi.5090060411 · Cited by 74 · see citing works
Analyzing Early At-Risk Factors in Higher Education E-Learning Courses.
Baker, R. S., Lindrum, D., Lindrum, M. J., et al. (2015) · Educational Data Mining · Cited by 54 · see citing works
Computer-Tailored Student Support in Introductory Physics
Huberth, M., Chen, P., Tritz, J., & McKay, T. A. (2015) · PLoS ONE · doi:10.1371/journal.pone.0137001 · Cited by 53 · see citing works
Predicting students’ satisfaction using a decision tree
Skrbinjek, V., & Dermol, V. (2019) · Tertiary Education and Management · doi:10.1007/s11233-018-09018-5 · Cited by 32 · see citing works
Student Success Prediction and the Trade-Off between Big Data and Data Minimization
Heuer, H., & Breiter, A. (2018) · DeLFI · Cited by 30 · see citing works
Releasing Personal Information Within Learning Analytics Systems
Ifenthaler, D., & Schumacher, C. (2019) · Springer eBooks · doi:10.1007/978-3-030-15130-0_1 · Cited by 29 · see citing works
Forecasting students' success in an open university
Kostopoulos, G., Kotsiantis, S. B., Pierrakeas, C. J., et al. (2018) · International Journal of Learning Technology · doi:10.1504/ijlt.2018.091630 · Cited by 28 · see citing works
Nichols, A. J., & Levy, Y. M. (2008) · The Internet and Higher Education · doi:10.1016/j.iheduc.2008.10.003 · Cited by 27 · see citing works
Preidys, S., & Sakalauskas, L. L. (2010) · Technological and Economic Development of Economy · doi:10.3846/tede.2010.06 · Cited by 26 · see citing works
Logistic Regression Analysis on Learning Behavior and Learning Effect Based on SPOC Data
Ran, J., Zhang, G., Zheng, T., et al. (2018) · International Conference on Computer Science & Education (ICCSE) · doi:10.1109/iccse.2018.8468834 · Cited by 13 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Ouyang, F., Zheng, L., & Jiao, P. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-10925-9 · Cited by 831 · see citing works
Guerrero-Roldán, A.-E., Rodríguez-González, M. E., Bañeres, D., Elasri-Ejjaberi, A., & Cortadas, P. (2021) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-021-00271-0 · Cited by 50 · see citing works
- Authors
- Mubarak, A. A., Cao, H., & Zhang, W.
- Affiliations
- Ibb University (YE)
- Shaanxi Normal University (CN)
- School of Computer and Science, Shaanxi Normal University, Xian, People’s Republic of China
- Corresponding author
- Cao, Han
- Venue
- Interactive Learning Environments
- Publisher
- Informa UK Limited
- Published
- 2020-02-19
- Volume
- 30
- Issue
- 8
- Pages
- 1414-1433
- ISSN
- 1049-4820 (ISSN-L)
- 1744-5191
- OpenAlex
- W3007158916
- Cited by
- 170
- References
- 44
- Citations by year
- 2026: 20 · 2025: 22 · 2024: 37 · 2023: 36 · 2022: 31 · 2021: 17 · 2020: 7
- FWCI
- 20.90
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Interactive Learning Environments (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Demography › Evasion and Academic Success Factors
- Social Sciences › Education › Online and Blended Learning
- Keywords
- student dropout prediction, early dropout, online learning, at-risk students, interaction logs, student retention, higher education, logistic regression, input-output hidden Markov model, sequence labeling, time series forecasting
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 ›
Are you an author, or do you see a problem with this page? Request a review