Temporal Analytics of Workplace-Based Assessment Data to Support Self-regulated Learning

Abstract

One of the most effective ways to develop self-regulated learning skills in higher education is to include work placements. Workplace-based assessment (WBA) provides opportunities for students to gain feedback on their practical skills, reflect on their performance, and set goals and actions for further development. This requires identifying temporal patterns, as placements usually span extended periods of time. In this paper we explore two intelligent computational methods (burst detection and process mining) to derive temporal patterns. We apply both methods on WBA data from a cohort of first-year medical students. Through this we identify interesting temporal patterns, and gather educators’ feedback on their usefulness for self-regulated learning.