期刊名称：Australasian Journal of Educational Technology
印刷版ISSN：1449-3098
电子版ISSN：1449-5554
出版年度：2021
卷号：37
期号：4
页码：130-144
DOI：10.14742/ajet.6116
语种：English
出版社：Australasian Society for Computers in Learning in Tertiary Education
摘要：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.
