Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns

ABSTRACT

Computer-supported collaborative concept mapping (CSCCM) integrates technology and concept mapping to support students’ knowledge understanding, and much research on the behavioral patterns involved in CSCCM activities has been conducted. However, there is limited understanding of the differences in knowledge understanding and behavioral patterns between students with different levels of collaboration perception. This study examined the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns in the CSCCM activity. A total of 36 individuals from the same university participated in this study. The findings suggested that compared with students with a low level of collaborative perception, students with a high level of collaborative perception could obtain better conceptual knowledge understanding. However, there was no significant difference in factual knowledge understanding between students with different levels of collaboration perception. For behavioral patterns, students with a high level of collaboration perception demonstrated more diverse behavioral transition sequences, students with a middle level of collaboration perception demonstrated more repetitive behavioral sequences, and students with a low level of collaboration perception demonstrated less behavioral transition sequences. The findings of this research can provide a reference for teachers to design CSCCM activities in the classroom.

KEYWORDS:

• Concept mapping
• computer-supported collaborative learning
• factual knowledge understanding
• conceptual knowledge understanding
• behavioral pattern

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant number: 61977030, 62077017, 61937001].

Notes on contributors

Sannyuya Liu

Sannyuya Liu is a professor and an associate director in National Engineering Research Center for E-learning (NERCEL) and National Engineering Laboratory for Educational Big Data (NELEBD), Central China Normal University (CCNU). His research interests include education big data, smart education, education technology.

Lingyun Kang

Lingyun Kang is a doctoral student in National Engineering Research Center for E-learning (NERCEL), Central China Normal University (CCNU), China. Her research interests are learning analytics and collaborative learning.

Zhi Liu

Zhi Liu is an associate professor in National Engineering Laboratory for Educational Big Data (NELEBD), Central China Normal University (CCNU), China since 2015. His focuses include learning analytics and educational data mining. He was a guest researcher in the Department of Computer Science at Humboldt University of Berlin from 2017 to 2018. As a PI, he is leading the research projects of the NSFC “Research on Methods of Recognition and Adaptive Intervention of Cognitive Behavioral Patterns in SPOC Interactive Discourses”.

Jing Fang

Jing Fang is a doctoral student in National Engineering Research Center for E-learning (NERCEL), Central China Normal University (CCNU), China. Her research interests include learning behavior analysis, the teaching strategies to promote learners’ higher-order thinking ability, and teaching application of Concept Map.

Zongkai Yang

Zongkai Yang is a professor and director in National Engineering Research Center for E-learning (NERCEL) and National Engineering Laboratory for Educational Big Data (NELEBD), Central China Normal University (CCNU). He has long been engaged in the research and development of electronic Learning technology, computer network communication, and key technologies of modern service industry.

Jianwen Sun

Jianwen Sun is an associate professor in National Engineering Laboratory for Educational Big Data (NELEBD), Central China Normal University (CCNU). His research interests include knowledge graph, learning recommendation.

Meiyi Wang

Meiyi Wang is currently working toward the master’s degree in education technology in National Engineering Research Center for E-learning (NERCEL), Central China Normal University (CCNU), China. Her research interests are learning analytics and educational data mining.

Mengwei Hu

Mengwei Hu is currently working toward the master’s degree in education informational technology in National Engineering Research Center for E-learning (NERCEL), Central China Normal University (CCNU), China. Her research interests are learning analytics and learning performance prediction modeling.