Discovering Patterns in Workplace-based Assessment to Support Mentoring for Self-Regulated Learning

Supporting self-regulated learning is an open challenge in higher education, particularly when assessment takes place in the workplace over an extended period of time. The myPAL project addresses this challenge in a medical education context by developing a digital companion for self-regulated learning. We present multi-faceted learner modelling methods to discover useful patterns that can inform mentoring interventions for self-regulated learning enabled by workplace based assessment. Two types of methods -- text mining and temporal analytics -- are applied on workplace-based assessment data from a cohort of first-year medical students. Educators' feedback provides insights on the usefulness of these methods to support students' self-regulated learning and to give tutors an overview of the placement process