Mining Process Models from Workflow Logs
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Workflow mining: a survey of issues and approaches
Data & Knowledge Engineering
Clustering and Sequential Pattern Mining of Online Collaborative Learning Data
IEEE Transactions on Knowledge and Data Engineering
CSCL'07 Proceedings of the 8th iternational conference on Computer supported collaborative learning
Using process mining to identify models of group decision making in chat data
CSCL'09 Proceedings of the 9th international conference on Computer supported collaborative learning - Volume 1
Content analysis: What are they talking about?
Computers & Education - Methodological issue in researching CSCL
Regulative processes in individual, 3D and computer supported cooperative learning contexts
Computers in Human Behavior
Fuzzy mining: adaptive process simplification based on multi-perspective metrics
BPM'07 Proceedings of the 5th international conference on Business process management
Computers in Human Behavior
Review: Educational data mining: A survey and a data mining-based analysis of recent works
Expert Systems with Applications: An International Journal
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The 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 90min 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.