.png){kind=icon}
.png){kind=icon}
Return
Exploring regulatory processes during a computer-supported collaborative learning task using process mining
.png){kind=icon}
PRE
AI
DOI：10.1016/j.chb.2012.02.016.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The purpose purpose of this study was to explore sequences of social regulatory processes during a computer-supported collaborative learning task and their relationship to group performance. Analogous to self-regulation during individual learning, we conceptualized social regulation both as individual and as collaborative activities of analyzing, planning, monitoring and evaluating cognitive and motivational aspects during collaborative learning. We analyzed the data of 42 participants working together in dyads. They had 90 min to develop a common handout on a statistical topic while communicating only via chat and common editor. The log files of chat and editor were coded regarding activities of social regulation. Results show that participants in dyads with higher group performance (N = 20) did not differ from participants with lower group performance (N = 22) in the frequencies of regulatory activities. In an exploratory way, we used process mining to identify process patterns for high versus low group performance dyads. The resulting models show clear parallels between high and low achieving dyads in a double loop of working on the task, monitoring, and coordinating. Moreover, there are no major differences in the process of high versus low achieving dyads. Both results are discussed with regard to theoretical and empirical issues. Furthermore, the method of process mining is discussed. (C) 2012 Elsevier Ltd. All rights reserved.
Keywords:
Computer-supported collaborative learning
Social regulation
Research methods
Self-regulated learning
Process mining
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
8.9
Papers:
9.0K
Citations:
5.8W