Exploring the co‐occurrence of students' learning behaviours and reasoning processes in an intelligent tutoring system: An epistemic network analysis.

Background: Medical students use a variety of self‐regulated learning (SRL) strategies in different medical reasoning (MR) processes to solve patient cases of varying complexity. However, the interplay between SRL and MR processes is still unclear. Objectives: This study investigates how self‐regulated learning (SRL) and medical reasoning (MR) occurred concurrently in medical students while completing a diagnostic task in an intelligent tutoring system. This study aims to provide new insights into performance differences between high‐ and low‐achieving students in tasks of varying complexity. Methods: Thirty‐one medical students (67.6% female) from a large North American university were tasked with solving two virtual patient cases in an intelligent tutoring system, BioWorld. BioWorld was designed for medical students to practice clinical reasoning skills deliberately. We collected students' think‐aloud protocols, based on which we coded their use of SRL behaviours and medical reasoning activities. We analysed the co‐occurrences of SRL behaviours and medical reasoning activities using the epistemic network analysis (ENA) method. Results: The SRL behaviour self‐reflection and MR activity lines of reasoning co‐occurred more frequently in a difficult task than in an easy task. In both tasks, high performers demonstrated more co‐occurrences of self‐reflection and lines of reasoning than low performers. Moreover, the MR activity conceptual operations co‐occurred more frequently with the SRL activities of monitoring and evaluation among high performers compared to low performers in an easy task. Implications: The co‐occurrences of SRL behaviours and MR processes account for students' performance differences. The design of computer‐based learning environments for clinical reasoning should promote the acquisition of both SRL and medical reasoning abilities. Moreover, medical educators should consider task complexity when scaffolding. Lay Description: What is already known about this topic: Self‐regulated learning (SRL) and medical reasoning skills are both crucial for diagnosing patients.Medical students can practice clinical reasoning with computer simulations. What this paper adds: Students solved virtual patients of varying complexity in an intelligent tutoring systemWe examined the co‐occurrences of SRL behaviours and medical reasoning process.Epistemic network analysis was used to analyse the interplay of SRL and medical reasoning.High performers show more co‐occurrences of reflection and higher‐order reasoning. Implications for practice and/or policy: Task complexity has impact on students' learning and reasoning co‐occurrences.Intelligent tutoring systems should foster regulation and reasoning acquisition. [ABSTRACT FROM AUTHOR]