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Self-regulated learning as a complex dynamical system: Examining students' STEM learning in a simulation environment
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DOI：10.1016/j.lindif.2022.102144.png)
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Abstract
En 中文
Self-regulated learning (SRL) is essentially a complex dynamical system (CDS). However, no effort has been made to study SRL from a CDS approach in the context of science learning. In this study, we adopted the ideas and analytical techniques of complexity science to analyze SRL. Specifically, 74 ninth-grade students were asked to undertake an engineering design task in a computer-simulated environment. We compared the differences in the complexity of the SRL process and the regularity of SRL behaviors between the high and low performers. We found that the SRL processes of the high performers were more complex than those of the low performers. In general, the low performers demonstrated a higher degree of repetition of SRL behaviors than the high performers. The low performers were also more likely to exhibit a behavior repeatedly than the high performers. This study extends the literature on the dynamics of SRL in both theoretical and methodological dimensions.
Keywords:
Self-regulated learning
Complex dynamical system
Recurrence quantification analysis
SRL dynamics
Computer-simulated environment
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