https://orcid.org/0000-0002-4896-8198
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ABSTRACT
Research on self-regulated learning (SRL) in engineering design is growing. While SRL is an effective way of learning, however, not all learners can regulate themselves successfully. There is a lack of research regarding how student characteristics, such as science knowledge and design knowledge, interact with SRL. Adapting the SRL theory in the field of engineering design, this study proposes a research model to examine the mediation and causal relationships among science knowledge, design knowledge, and SRL activities (i.e. observation, formulation, reformulation, analysis, evaluation). Partial least squares modeling was utilized to examine how the science and design knowledge of 108 ninth-grade participants interacted with their SRL activities in the process of performing an engineering task. Results reveal that prior science and design knowledge positively predict SRL activities. They also show that reformulation and analysis are the two SRL activities that can lead to an improvement in post science and design knowledge, but excessive observation can hinder post design knowledge. These results have important implications for the construction of learning environments to support SRL based on students’ prior knowledge levels.
Acknowledgements
Any opinions, findings, and conclusions or recommendations expressed in this paper, however, are those of the authors and do not necessarily reflect the views of the NSF. The authors are indebted to Joyce Massicotte, Elena Sereiviene, Jie Chao, and Corey Schimpf for assistance and suggestions.
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No potential conflict of interest was reported by the author(s).
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The data will be available by individual application directly to the author.
The research was approved by the Ethics Committee of the (omitted for anonymous review).
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Notes on contributors
Juan Zheng
Juan Zheng is a PhD candidate and research assistant at Educational Counselling and Psychology (ECP), McGill University with background in educational technology and learning sciences. Her research interests are self-regulated learning, academic emotions, and educational data mining
Wanli Xing
Wanli Xing is an Assistant Professor in Educational Technology at University of Florida, USA with background in learning sciences, statistics, computer science and mathematical modeling. His research interests are educational data mining, learning analytics, and CSCL.
Xudong Huang
Xudong Huang is a postdoctoral researcher in the Concord Consortium, USA with background in learning sciences and cognitive psychology. Her research interests are science and engineering education, artificial intelligence, educational data mining, and learning analytics.
Shan Li
Shan Li is a doctoral student in the Faculty of Education at McGill University, and is currently a member of the ATLAS (Advanced Technologies for Learning in Authentic Settings) Lab. His research focuses on the development of intelligent teaching and learning systems to promoting the integration of technology in K-12 education.
Guanhua Chen
Guanhua Chen is a postdoctoral researcher in the Concord Consortium. His research interests are science and engineering education research, computational thinking, machine learning, and educational data mining.
Charles Xie
Charles Xie is a senior research scientist in the Concord Consortium. His main research areas include computational science, learning science, data mining, machine learning, artificial intelligence, computer-aided design, scientific visualization, virtual reality, mixed reality, Internet of Things, molecular simulation, solar energy engineering, and infrared imaging.