Evolutionary Clustering of Apprentices' Self- Regulated Learning Behavior in Learning Journals

Learning journals are increasingly used in vocational education to foster self-regulated learning and reflective learning practices. However, for many apprentices, documenting working experiences is a difficult task. In this article, we profile apprentices' learning behavior in an online learning journal. Based on a pedagogical framework, we propose a novel multistep clustering pipeline that integrates different learning dimensions into a combined profile. Specifically, the profiles are described in terms of effort, consistency, regularity, help-seeking behavior, and quality of the written entries. Our results on two populations of chef apprentices (183 apprentices) interacting with an online learning journal (over 121K entries) show that our pipeline captures changes in learning patterns over time and yields interpretable profiles that can be related to academic performance. The obtained profiles can be used as a basis for personalized interventions, with the ultimate goal of improving the apprentices' learning experience

Identifying and Comparing Multi-dimensional Student Profiles Across Flipped Classrooms

Flipped classroom (FC) courses, where students complete pre-class activities before attending interactive face-to-face sessions, are becoming increasingly popular. However, many students lack the skills, resources, or motivation to effectively engage in pre-class activities. Profiling students based on their pre-class behavior is therefore fundamental for teaching staff to make better-informed decisions on the course design and provide personalized feedback. Existing student profiling techniques have mainly focused on one specific aspect of learning behavior and have limited their analysis to one FC course. In this paper, we propose a multi-step clustering approach to model student profiles based on pre-class behavior in FC in a multi-dimensional manner, focusing on student effort, consistency, regularity, proactivity, control, and assessment. We first cluster students separately for each behavioral dimension. Then, we perform another level of clustering to obtain multi-dimensional profiles. Experiments on three different FC courses show that our approach can identify educationally-relevant profiles regardless of the course topic and structure. Moreover, we observe significant academic performance differences between the profiles