Profiling self-regulation behaviors in STEM learning of engineering design. (January 2020)

• Record Type: Journal Article
• Authors: Zheng, Juan; Xing, Wanli; Zhu, Gaoxia; Chen, Guanhua; Zhao, Henglv; Xie, Charles
• Is Part Of: Computers & education. Volume 143(2020)
• Publication Date: 2020-01
• Subjects: Self-regulation -- STEM learning -- Learning analytics -- Energy3D -- Engineering design; Education -- Data processing -- Periodicals; Education -- Periodicals; Computers -- Periodicals; Computer-Assisted Instruction -- Periodicals; Éducation -- Informatique -- Périodiques; Electronic journals; 370.285
• DOI: 10.1016/j.compedu.2019.103669
• Languages: English
• ISSNs: 0360-1315

Abstract

Abstract: Engineering design is a complex process which requires science, technology, engineering, and mathematic (STEM) knowledge. Students' self-regulation plays a critical role in interdisciplinary tasks. However, there is limited research investigating whether and how self-regulation leads to different learning outcomes among students in engineering design. This study analyzes the engineering design behaviors of 108 ninth-grade U.S. students using principal component analysis and cluster analysis. It classifies the students into four distinct types: competent, cognitive-oriented, reflective-oriented, and minimally self-regulated learners. Competent self-regulated learners perceived themselves as the most self-regulated learners and had the greatest learning gains, although they did not perform best in the task. Cognitive-oriented self-regulated learners perceived themselves as the least self-regulated learners although they were the second best in both the performance of the task and learning gains. In contrast, reflective learners had the best performance in the task. Minimally self-regulated learners did not perform well in the task and had the lowest learning gains. The results revealed that the competent self-regulated learners had an appropriate assessment of themselves to obtain knowledge, cognitive-oriented self-regulated learners underestimated themselves, reflective learners focused on the results of the task, and minimally self-regulated learners overestimated themselves and exerted the least effort. The results also offer new insights into STEM education and self-regulated learning with emerging learning analytics. Highlights: A self-regulated learning model was developed in STEM education. This examined why and how self-regulated learning functioned in engineering design. We identified four unique behavioral profiles of self-regulated learners.