A scoping review of empirical studies on theory-driven learning analytics

Abstract

The field of learning analytics (LA) is developing rapidly. However, previous empirical studies on LA were largely data-driven. Little attention has been paid to theory-driven LA studies. The present scoping review identified and summarized empirical theory-driven LA studies, aiming to reveal how theories were integrated into LA. The review examined 37 peer-reviewed journal articles published from 2016 to 2020 from six databases. Results show that most studies were guided by the theories of self-regulated learning and social constructivism; most integrated theories into LA for better interpreting the data analysis results; and most linked theoretical constructs and log variables directly. Several studies employed well-developed survey instruments to measure theoretical constructs. The review results indicate that LA studies still need to strive for new theory advances in learning. Recommendations for future study are discussed.

Keywords:

• learning analytics
• log analysis
• online learning
• education
• learning theory

Acknowledgment

We would like to thank the editor and reviewers for their invaluable feedback on the previous version of the article.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Qin Wang

Qin Wang is a PhD student in the Department of Educational Psychology and Special Education, College of Education, University of Saskatchewan. Her research focuses on assessment literacy and the applications of machine learning in education, including learning analytics and data mining.

Amin Mousavi

Amin Mousavi is an associate professor of psychometrics, classroom assessment, and measurement in the Department of Educational Psychology and Special Education, College of Education, University of Saskatchewan. His main area of interest for teaching and research is psychometrics and quantitative methodology in social and behavioral sciences.

Chang Lu

Chang Lu is an assistant professor in the School of Education, Shanghai Jiao Tong University. Her research focuses on computational thinking and applications of machine learning in education, including computer-based testing, automated essay scoring, automated feedback generation, data mining, and learning analytics.