The Temporal Structures and Sequential Patterns of Self-Regulated Learning Behaviors in Clinical Reasoning (Poster 21)

• In Event: AERA Virtual Poster Session 9
  In Poster Session: Investigations Into Self-Regulation of Learning and Related Constructs

Abstract

We used recurrence quantification analysis and sequential analysis to analyze the temporal structures and sequential patterns of students’ self-regulated learning (SRL) behaviors, as they diagnosed a virtual patient in an intelligent tutoring system. We found that low performers had more single, isolated recurrent behaviors, whereas the recurrent behaviors of high performers were more likely to be part of a behavioral sequence. High performers also demonstrated a higher transition probability across the three phases of SRL than low performers. In addition, high performers were unique in that their behavioral state transitions were cyclically sustained. This study provided theoretical insights regarding the cyclical nature of SRL. This study has also methodological contributions to the analysis of the temporal structures of SRL behaviors.

Authors

• Shan Li, Lehigh University

• Juan Zheng, Lehigh University

• Susanne P. Lajoie, McGill University