Computer-supported collaborative concept mapping
The impact of task types on learners’ knowledge understanding and behavioral patterns
Bibliographic Data
| ID | 21631347 |
|---|---|
| Authors | Lingyun Kang (0000-0002-1520-3594, Jiangxi Normal University), Wanli Zhao (0000-0001-8600-4720, Wuhan University, corresponding author), Zixuan Zhang (0009-0000-1281-0324, Jiangxi Normal University), Z H Zhang (0009-0006-2313-5743, Nanchang Normal University) |
| Year | 2025 |
| Pages | 1-15 |
| Publication date | 2025-11-26 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Interactive Learning Environments (JOURNAL) |
| Journal identifiers | ISSN: 1049-4820 • E-ISSN: 1744-5191 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/10494820.2025.2583195 |
| OpenAlex | W4416717248 |
| Language | EN |
| References cited | 25 |
Computer-supported collaborative concept mapping (CSCCM) integrates concept mapping with technology to support collaborative group learning and conceptual understanding. This study employed a within-subjects experimental design to examine how task type influences learners’ knowledge understanding and behavioral patterns in CSCCM activities. Thirty-four third-year undergraduates completed three tasks – analyze, evaluate, and create – in a CSCCM environment. The results reveal that (1) regarding knowledge understanding, the number of crosslinks per learner in the analyze task was significantly greater than that in the create task; (2) for operational behavioral patterns, learners displayed a distinctive transition sequence, DL (deleting links) → AN (adding nodes), in the analyze task that did not occur in the evaluate or create tasks; and (3) for conversational behavioral patterns, learners engaged in more cognitive-conflict dialog during the create task than during the analyze and evaluate tasks. These findings inform the design of CSCCM activities and the development of adaptive concept mapping systems. Teachers can use collaboration scripts, role assignments, and prompts to reduce cognitive load and enhance engagement. Developers should focus on adaptive tools with real-time suggestions, usability improvements, and automated feedback to refine learners' understanding
Cognition · Collaborative learning · Electronic learning · Knowledge level · Task Analysis · Innovative Teaching and Learning Methods · Science Education and Pedagogy · Visual and Cognitive Learning Processes
Observing Interaction
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Exploratory study of an AI-supported discussion representational tool for online collaborative learning in a Chinese university
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Concept mapping in computer-supported learning environments
Knowledge map construction based on association rule mining extending with interaction frequencies and knowledge tracking for rules cleaning
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Task complexity affects temporal characteristics of self-regulated learning behaviours in an intelligent tutoring system
Hierarchical clustering of groups’ collaborative discourses during the computer-supported collaborative concept mapping process
Profiling students’ learning engagement in Mooc discussions to identify learning achievement
Learning performance and behavioral patterns of online collaborative learning
Analysing students' concept mapping style and its association with task performance in computer‐based inquiry learning
Exploring the influence of students' perceptions of face-to-face collaboration on cognitive engagement and learning outcomes in collaborative programming
Group concept mapping strategies for collaborative continuation tasks
| Citation velocity | historical |
|---|---|
| Highly cited | No |