An integrated approach for knowledge extraction and analysis in collaborative knowledge construction

Collaborative knowledge construction (CKC) involved students' sharing of information, improvement of ideas, and construction of collective knowledge. In this process, knowledge extraction and analysis can provide valuable insights into students' knowledge capacities, depths, and levels in order to improve the CKC quality. However, existing studies tended to extract and analyze knowledge from a single perspective (e.g., the number of certain knowledge types and knowledge structures), which failed to demonstrate the complexity and dynamics of knowledge construction and advancement. To fill this gap, this research designed a series of computer-supported collaborative concept mapping (CSCCM) activities to facilitate students' CKC process and then used an integrated approach (i.e., semantic knowledge analysis combined with learning analytics) to extract, analyze, and understand students' knowledge characteristics and evolutionary trends. Results demonstrated that compared to the low-performing pairs, the high-performing pairs mainly discussed knowledge related to the course content, and their knowledge evolution trend was relatively stable. Based on the results, this research provided analytical implications to extract, analyze, and understand students' knowledge and pedagogical implications to promote students' knowledge construction and advancement.