Exploring the Patterns of Socially Shared Metacognition in Computer-Supported Collaborative Learning

• In Event: Computer-Supported Collaborative Learning

Abstract

The purpose of this study was to discover students’ socially shared metacognition (SSM) patterns in a holistic way in an electronics course supported by a CSCL environment and to understand how SSM patterns influenced the task performance. To investigate patterns of SSM behaviors, 126 collaborative electronics learning scenarios were manually coded. Then K-Means clustering was conducted. As a result, four clusters were discovered for collaborative problem solving. Interaction between the four identified patterns with the final performance, using Chi-squared test, was examined. There was a significant difference on the distribution of success and failure groups among the clusters. These results have both theoretical implications to advance our understanding of SSM and practical implications to promote better collaborative problem solving skills.

Authors

• Okan Arslan, Texas Tech University

• Wanli Xing, Texas Tech University

• Tugba Kamali Arslantas, Aksaray University

• Paul Horwitz, The Concord Consortium

• Cynthia McIntyre, The Concord Consortium