The importance of students’ motivational dispositions for designing learning analytics

Abstract

Depending on their motivational dispositions, students choose different learning strategies and vary in their persistence in reaching learning outcomes. As learning is more and more facilitated through technology, analytics approaches allow learning processes and environments to be analyzed and optimized. However, research on motivation and learning analytics is at an early stage. Thus, the purpose of this quantitative survey study is to investigate the relation between students’ motivational dispositions and the support they perceive through learning analytics. Findings indicate that facets of students’ goal orientations and academic self-concept impact students’ expectations of the support from learning analytics. The findings emphasize the need to design highly personalized and adaptable learning analytics systems that consider students’ dispositions and needs. The present study is a first attempt at linking empirical evidence, motivational theory, and learning analytics.