Modeling temporal self-regulatory processes in STEM learning of engineering design

Abstract

Self-regulation is crucial for student success in scientific inquiry and engineering design. However, it remains unclear how students dynamically engage in self-regulated learning (SRL) processes to achieve high performance. In this study, we investigated the temporal nature of self-regulation during engineering design by leveraging computer trace data from 101 high school students who designed an energy-plus house in a simulated learning environment. Using sequential mining, we found that high-performing students were more engaged in the Observation, Analysis, and Evaluation phases of SRL than low-performing students. Additionally, high-performing students demonstrated consecutive sequential patterns between Observation and Analysis, Reformation and Evaluation, and Analysis and Evaluation behaviors. These findings provide insights into students’ SRL processes and the design of scaffoldings.

Keywords

• sequential mining
• self-regulated learning
• engineering design
• stem learning