Embedding Prompts to Facilitate Small Groups’ Computer-Supported Collaborative Concept Mapping

Abstract

Collaborative knowledge construction (CKC) engages students work in small groups to complete specific tasks that are often open-ended, ill-structured, and beyond the knowledge and skills of the individual students in the group. Computer-supported collaborative concept mapping (CSCCM) with the support of prompts can be used to facilitate a high quality of CKC. However, previous empirical research showed complicated effects of prompts on the quality of CKC in the CSCCM environment. In this research, we developed a CSCCM platform customized with three CKC conditions (i.e., no prompt, agreement prompt, and disagreement prompt) to examine the effects of different prompts on CKC performance, process, and student perception. Results showed that the embedded disagreement prompt had the best effect on improving the quality of CKC among three conditions, regarding the peer interaction, cognitive engagement, behavioral contribution and group performance. Based on the results, this research provided theoretical implication related to transactivity, pedagogical strategy of considering advantages and disadvantages of agreement and disagreement prompts, as well as analytical implication of collecting and analyzing multi-source data with mixed methods for future research and practice.

Keywords:

• Computer-supported collaborative learning
• collaborative knowledge construction
• computer-supported collaborative concept mapping
• prompts
• learning analytics

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by National Natural Science Foundation of China (62177041); Zhejiang Province educational science and planning research project (2022SCG256); Zhejiang University graduate education research project (20220310), and by the Fundamental Research Funds for the Central Universities, Zhejiang University, China.

Notes on contributors

Fan Ouyang

Fan Ouyang is a research professor in College of Education at Zhejiang University. Her research interests are computer-supported collaborative learning, learning analytics and educational data mining, and AI in education.

Luyi Zheng

Luyi Zheng is a master student in College of Education at Zhejiang University, under the supervision of Fan Ouyang. Her research interests are collaborative knowledge construction, and learning analytics.