Cluster Investigation of Socially Shared Metacognition in Computer Supported Collaborative Learning

• In Event: AECT Gallery Walk - D&D Showcase, Emerging Technology Showcase, AECT Gallery Walk - Available 24/7 during the convention.
  In Poster Session: GSA Poster Sessions

Short Description

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 computer supported collaborative learning (CSCL) environment and to understand how SSM patterns influenced the task performance. Machine learning algorithm was used to extract SSM behaviors by using log data contain every action and chat sessions they did. Then K-Means clustering was conducted to investigate both process and quality of performances.

Author

• Presenter: Okan Arslan, Texas Tech University