Gorgun, Guher, Seyma N. Yildirim-Erbasli, and Carrie Demmans Epp. "Predicting Cognitive Engagement in Online Course Discussion Forums." International Educational Data Mining Society (2022).

Identifying student cognitive engagement in online learning is crucial for ensuring success, but it can be challenging due to the nature of the online environment. This study developed predictive models to automate the identification of cognitive engagement in online discussion posts by adapting the Interactive, Constructive, Active, and Passive (ICAP) Engagement theory and merging it with Bloom's taxonomy. Using data from 4,217 student posts, machine learning classifiers (decision tree, random forest, and support vector machine) were trained to identify engagement levels. The support vector machine model performed best, suggesting that automatic identification of cognitive engagement is feasible and could aid in providing timely support and adapting teaching practices.