Impact of Learning Analytics Guidance on Student Self-Regulated Learning Skills, Performance, and Satisfaction: A Mixed Methods Study

Abstract

Learning analytics (LA) involves collecting, processing, and visualizing big data to help teachers optimize learning conditions. Despite its contributions, LA has not yet been able to meet teachers’ needs because it does not provide sufficient actionable insights that emphasize more on analytics and less on learning. Our work uses specific analytics for student guidance to evaluate an instructional design that focuses on LA agency between teachers and students. The research goal is to investigate whether the minimal and strong guidance provided by the LA learning approach has the same impact on student outcomes. The research questions are as follows “Does the LA-based minimal and strong guidance learning approach have the same impact on student performance and SRL skills? What are the students’ learning perceptions and satisfaction under LA-based guidance?” A mixed methods study was conducted at a university in which LA-based strong guidance was applied to the experimental group and minimal guidance was given to the control group. When strong guidance was applied, the results indicated increased final grades and SRL skills (metacognitive activities, time management, persistence, and help seeking). Furthermore, student satisfaction was high with LA-based guidance. Future research could adapt our study to nonformal education to provide nuanced insights into student outcomes and teachers’ perceptions.