Predicting at-risk university students based on their e-book reading behaviours by using machine learning classifiers

Providing early predictions of academic performance is necessary for identifying at-risk students and subsequently providing them with timely intervention for critical factors affecting their academic performance. Although e-book systems are often used to provide students with teaching/learning materials in university courses, seldom has research made the early prediction based on their online reading behaviours by implementing machine learning classifiers. This study explored to what extent university students’ academic achievement can be predicted, based on their reading behaviours in an e-book supported course, using the classifiers. It further investigated which of the features extracted from the reading logs influence the predictions. The participants were 100 first-year undergraduates enrolled in a compulsory course at a university in Taiwan. The results suggest that logistic regression Gaussian naïve Bayes, supports vector classification, decision trees, and random forests, and neural networks achieved moderate prediction performance with accuracy, precision, and recall metrics. Furthermore, the Bayes classifier identified almost all at-risk students. Additionally, student online reading behaviours affecting the prediction models included: turning pages, going back to previous pages and jumping to other pages, adding/deleting markers, and editing/removing memos. These behaviours were significantly positively correlated to academic achievement and should be encouraged during courses supported by e-books. Implications for practice or policy: • For identifying at-risk students, educators could prioritise using Gaussian naïve Bayes in an e-book supported course, as it shows almost perfect recall performance. • Assessors could give priority to logistic regression and neural networks in this context because they have stable achievement prediction performance with different evaluation metrics. • The prediction models are strongly affected by student online reading behaviours, in particular by locating/returning to relevant pages and modifying markers.