LEARNING ANALYTICS AND STUDENTS’ MOTIVATION: A SYSTEMATIC REVIEW

Abstract

The growing integration of digital learning environments in higher education has intensified interest in learning analytics as a means of supporting student learning processes. Among the outcomes associated with learning analytics, student motivation has received increasing attention due to its critical role in academic engagement, persistence, and success. This systematic review synthesises empirical evidence on the relationship between learning analytics and students’ motivation in higher education. Following the PRISMA 2020 guidelines, a comprehensive search was conducted across Scopus, Web of Science, ERIC, ScienceDirect, SAGE Journals, and Google Scholar. After screening and eligibility assessment, 24 empirical studies published between 2015 and 2025 were included in the final qualitative thematic synthesis. The findings indicate a generally positive association between learning analytics and student motivation, particularly in relation to self-efficacy, self-regulated learning, and academic motivation. Learning analytics tools such as dashboards, analytics-based feedback, and progress indicators were found to enhance students’ awareness of learning progress and support goal-directed behaviour. However, the review also revealed conditional and mixed effects, with some studies reporting reduced motivation when analytics feedback was perceived as controlling or overly comparative. Methodological analysis showed a predominance of cross-sectional designs, limiting causal inference. Overall, the review highlights the motivational potential of learning analytics while underscoring the importance of pedagogically grounded, autonomy-supportive, and context-sensitive implementation. Implications for future research, educational practice, and policy are discussed.