Learning Analytics Acceptance in Higher Education: An Extension of the TAM with Motivational and Self-Regulatory Factors

Abstract

Amid the digital transformation of higher education, Learning Analytics (LA) systems are increasingly used to support self-regulated learning and provide personalized feedback. This study investigates key factors influencing students’ acceptance of such systems by extending the Technology Acceptance Model (TAM) with motivational and self-regulatory constructs. It also examines students’ preferences for different types of LA-based visual feedback, with a particular focus on social comparison features. The findings confirm the importance of usability and perceived usefulness, and identify attention control and time management as relevant predictors of LA acceptance. Contrary to initial assumptions, socially referenced feedback was perceived as less interesting than individualized formats. Limitations of the study include the cross-sectional design and the timing of data collection, which occurred before students had hands-on experience with the system. Nevertheless, the results provide important insights into learner-centered LA design and highlight the need for adaptable feedback strategies that align with individual preferences and regulatory capacities.