Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A network approach

Abstract

From a network perspective, self-regulated learning (SRL) can be conceptualized as networks of mutually interacting self-regulatory learning behaviors. Nevertheless, the research on how SRL behaviors dynamically interact over time in a network architecture is still in its infancy, especially in the context of STEM (sciences, technology, engineering, and math) learning. In the present paper, we used a multilevel vector autoregression (VAR) model to examine the temporal dynamics of SRL behaviors as 101 students designed green buildings in Energy3D, a simulation-based computer-aided design (CAD) environment. We examined how different performance groups (i.e., unsuccessful, success-oriented, and mastery-oriented groups) differed in SRL competency, actual SRL behaviors, and SRL networks. We found that the three groups had no significant difference in their perceived SRL competency; however, they differed in SRL behaviors of evaluation. Both the mastery-oriented and success-oriented groups performed more evaluation behaviors than the unsuccessful group. Moreover, the mastery-oriented group showed stronger interaction between SRL behaviors than the success-oriented group and the unsuccessful group. The SRL networks of the three groups shared some similarities, but they were different from each other in general. This study has significant theoretical and methodological implications for the advancement of research in SRL dynamics.