Identifying Student Engagement and Performance from Reading Behaviors in Open eBook Assessment

Abstract

Digitized learning materials are a core part of modern education and analysis of the use can offer insight into the learning behavior of high and low performing students. The topic of predicting student characteristics has gained a lot of attention in recent years, with applications ranging from affect to performance and at-risk student prediction. In this paper, we examine students reading behavior using a digital textbook system while taking an open ebook test from the perspective of engagement and performance to identify strategies that are used. We create models to predict the performance and engagement of learners before the start of the assessment and extract reading behavior characteristics employed before and after the start of the assessment in a higher education setting. It was found that compared to performance, the prediction of overall engagement has a higher accuracy, and therefore could be more appropriate for identify intervention candidates.