Learning analytics dashboard-based self-regulated learning approach for enhancing students’ e-book-based blended learning

Abstract

Blended learning (BL) combines traditional classroom activities with online learning resources, enabling students to obtain higher academic performance through well-defined interactive learning strategies. However, lacking the capacity to self-regulate their learning, many students might fail to comprehensively study the learning materials after face-to-face learning. In this study, a learning analytics dashboard (LAD)-based self-regulated learning (SRL) approach is proposed to enhance the students’ practices of SRL in an e-book-based BL environment. The proposed approach aims to support students to precisely reflect on their face-to-face e-book reading activities, effectively review the e-book learning materials after the face-to-face learning sessions, and, finally, set new goals for their next face-to-face learning session by using a LAD. To evaluate the effects of the proposed approach, a quasi-experimental design was deployed in a university-level course that adopted a BL model. The experimental group learned through the proposed approach using an e-book and the LAD, whereas the control group learned using the conventional BL approach using only the e-book. The results of the one-way analysis of covariance (ANCOVA) and Mann–Whitney U test demonstrate a statistically significant difference (p-value less than 0.01) between both groups in terms of students’ learning outcomes, awareness of SRL, self-efficacy (SE), and e-book reading engagements. This provides educators with evidence of the effectiveness of an explicit SRL approach in BL, which not only improves student learning outcomes from the given course and awareness of self-regulation and SE but also increases course engagement compared to students who learn with conventional BL approaches.