Student Modeling for Individuals and Groups: the BioWorld and HOWARD Platforms

The Relationship Between Self-Regulated Learning Competency and Clinical Reasoning Tendency in Medical Students

Self-regulated learning (SRL) is essential to professional learning and practice across disciplines. However, the literature provides limited insights into how medical educators could leverage the SRL framework to support trainees' strategic processes in clinical reasoning activities. In this study, we investigated the relationship between SRL competency and clinical reasoning tendency as 64 medical students diagnosed a virtual patient in a computer-simulated environment. We further examined whether students with different profiles of SRL competency and clinical reasoning tendency differed in their behavioral patterns and performance. The results suggested that SRL competency positively predicted clinical reasoning tendency. Enhancing medical students' SRL competency, especially their self-reflection skills, could increase the tendency toward relying on an analytic approach to clinical reasoning. Moreover, we identified two groups of students (i.e., analytic SRL learners, and non-analytic, low SRL learners) using K-means clustering analysis. The two groups of students differed in their behavioral patterns in clinical reasoning, as revealed by lag sequential analysis. Furthermore, analytic SRL learners ordered more relevant lab tests than non-analytic low SRL learners in clinical reasoning. This study has methodological and practical implications.

Lessons Learned and Future Directions of MetaTutor: Leveraging Multichannel Data to Scaffold Self-Regulated Learning With an Intelligent Tutoring System

Self-regulated learning (SRL) is critical for learning across tasks, domains, and contexts. Despite its importance, research shows that not all learners are equally skilled at accurately and dynamically monitoring and regulating their self-regulatory processes. Therefore, learning technologies, such as intelligent tutoring systems (ITSs), have been designed to measure and foster SRL. This paper presents an overview of over 10 years of research on SRL with MetaTutor, a hypermedia-based ITS designed to scaffold college students' SRL while they learn about the human circulatory system. MetaTutor's architecture and instructional features are designed based on models of SRL, empirical evidence on human and computerized tutoring principles of multimedia learning, Artificial Intelligence (AI) in educational systems for metacognition and SRL, and research on SRL from our team and that of other researchers. We present MetaTutor followed by a synthesis of key research findings on the effectiveness of various versions of the system (e.g., adaptive scaffolding vs. no scaffolding of self-regulatory behavior) on learning outcomes. First, we focus on findings from self-reports, learning outcomes, and multimodal data (e.g., log files, eye tracking, facial expressions of emotion, screen recordings) and their contributions to our understanding of SRL with an ITS. Second, we elaborate on the role of embedded pedagogical agents (PAs) as external regulators designed to scaffold learners' cognitive and metacognitive SRL strategy use. Third, we highlight and elaborate on the contributions of multimodal data in measuring and understanding the role of cognitive, affective, metacognitive, and motivational (CAMM) processes. Additionally, we unpack some of the challenges these data pose for designing real-time instructional interventions that scaffold SRL. Fourth, we present existing theoretical, methodological, and analytical challenges and briefly discuss lessons learned and open challenges.