Process Analysis of How Task Complexity Affects Students' Self-Regulated Learning in an Intelligent Tutoring System

• In Event: Self-Regulated Learning and Motivation in Technology

Abstract

Previous studies have employed process mining techniques to explore temporal dynamics of students’ self-regulated learning (SRL) behaviors; however, little is known about how task complexity determines SRL processes. In this study, we applied the Inductive visual Miner (IvM) to investigate SRL patterns as 27 medical students solved clinical problems of varying difficulty in BioWorld, a computer-based intelligent tutoring system (ITS). Process models revealed that participants were data-driven (i.e., firstly collecting evidence items) in the easy and moderate tasks but theory-driven (i.e., firstly proposing hypotheses) in the difficult task. Low performers demonstrated bidirectional loops between different SRL behaviors, but high performers showed unidirectional paths. This study can guide medical educators to provide adaptive prompts to facilitate clinical reasoning processes.

Authors

• Tingting Wang, McGill University

• Shan Li, Lehigh University

• Alejandra Ruiz-Segura, McGill University

• Chengyi Tan, McGill University

• Xiaoshan Huang, McGill University

• Susanne P. Lajoie, McGill University