Mining Sequential Learning Trajectories With Hidden Markov Models For Early Prediction of At-Risk Students in E-Learning Environments

• Published in: IEEE Transactions on Learning Technologies Vol. 15; no. 6; pp. 783 - 797
• Main Authors: Gupta, Anika, Garg, Deepak, Kumar, Parteek
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.12.2022; Institute of Electrical and Electronics Engineers, Inc; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Assessments; At Risk Students; Behavioral sciences; CAI; Classification; Computer assisted instruction; Data mining; Data models; Decision Making; Distance learning; e-learning environments; Early warning systems; Early warning systems (EWS); Educational Environment; Electronic learning; formative assessments; hidden Markov model (HMM); Hidden Markov models; Higher Education; Instructional Design; Intervention; Learning Analytics; Learning Management Systems; Markov chains; Markov Processes; Modelling; Online Courses; Optimization; Prediction; Predictive models; Sequential Learning; sequential pattern analysis; Student Records; Students; Sustainable Development; Teaching Methods; teaching/learning strategies; Virtual environments; Weight reduction
• ISSN: 1939-1382; 2372-0050
• DOI: 10.1109/TLT.2022.3197486

With the onset of online education via technology-enhanced learning platforms, large amount of educational data is being generated in the form of logs, clickstreams, performance, etc. These Virtual Learning Environments provide an opportunity to the researchers for the application of educational data mining and learning analytics, for mining the students learning behavior. This further helps them in data-driven decision making through timely intervention via early warning systems (EWS), reflecting and optimizing educational environments, and refining pedagogical designs. In this, the role of EWS is to timely identify the at-risk students. This study proposes a modeling methodology deploying interpretable Hidden Markov Model for mining of the sequential learning behavior built upon derived performance features from light-weight assessments. The public OULA dataset having diversified courses and 32 593 student records is used for validation. The results on the unseen test data achieve a classification accuracy ranging from 87.67% to 94.83% and AUC from 0.927 to 0.989, and outperforms other baseline models. For implementation of EWS, the study also predicts the optimal time-period, during the first and second quarter of the course with sufficient number of light-weight assessments in place. With the outcomes, this study tries to establish an efficient generalized modeling framework that may lead the higher educational institutes toward sustainable development.