Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment. (July 2019)

• Record Type: Journal Article
• Authors: Zheng, Juan; Xing, Wanli; Zhu, Gaoxia
• Is Part Of: Computers & education. Volume 136(2019)
• Page Start: 34
• Page End: 48
• Publication Date: 2019-07
• Subjects: Self-regulated learning -- Socially shared regulation -- STEM education -- Sequential mining; Education -- Data processing -- Periodicals; Education -- Periodicals; Computers -- Periodicals; Computer-Assisted Instruction -- Periodicals; Éducation -- Informatique -- Périodiques; Electronic journals; 370.285
• DOI: 10.1016/j.compedu.2019.03.005
• Languages: English
• ISSNs: 0360-1315

Abstract

Abstract: While the importance in collaborative learning of both self- and socially shared regulatory processes has increasingly been emphasized, little research has examined sequences of such processes and how they influence group performance. This study identified sequences of self- and socially shared regulatory activities in the online chats and logs of students completing a STEM task in a computer-supported collaborative learning (CSCL) environment. High school and college students (N = 156) were randomly assigned to groups of three and asked to solve four tasks of increasing complexity in a virtual learning environment designed to teach students about electronics. The results revealed that the students engaged mostly in executing, self-monitoring, and socially shared monitoring activities. The successful groups demonstrated more frequent and more diverse regulatory activities than did the less successful groups. A Markov chain analysis revealed that the successful groups were most likely to start with self-executing and end with socially shared monitoring, while the less successful group were most likely to start with executing and end with self-executing. The results of this study reveal that the timing of socially shared monitoring influences the success of collaborative learning, which have implications for teaching practices and for adaptive scaffolding group learners in CSCL. Highlights: A methodology for examining self- and socially shared regulation simultaneously. Identify and visualize differential sequential patterns using data mining techniques. Automatically identify regulatory activities using machine learning models. The timing of socially shared monitoring influences the success of collaborative learning.