Abstract
Conflicts play an important role to improve group learning effectiveness; they can be decreased, increased, or ignored. Given the sequence of messages of a collaborative group, we are interested in recognizing conflicts (detecting whether a conflict exists or not). This is not an easy task because of different types of natural language ambiguities. A conversation can be represented as a conversation graph; i.e., a direct multidigraph where the nodes are users, and an edge means a message. The approach proposed in this paper focuses on the emotional interactions of group members. Hence, to detect conflicts it analyzes emotions involved in the cycles of the graph. This strategy has the advantages of considering the sentiment of a sequence of messages to take a better decision and analyzing interactions with two or more participants. The proposed approach has been tested in collaborative learning tasks, achieving an F1 score of 92.6%, and a 90.1% recall score for conflicting situations. This approach can help teachers and students to improve the learning process.
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Find the latest articles, discoveries, and news in related topics.Data Availability
The dataset analyzed during the current study is available from the corresponding author upon reasonable request. The dataset used in this study is part of the previous work. It includes dialog messages from the Collab’s (tool to support collaborative learning) of undergraduate computer-sciences degree program students of Argentine and Colombia.
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pySentimiento - https//github.com/finiteautomata/pysentimiento/
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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. No funding was received to assist with the preparation of this manuscript.
The authors worked in the affective computing area, specifically in speech emotion recognition for the control of difficulty in an educational video game, and other research in facial emotion recognition for medical records, now sentiment analysis is another research in the process. Moreover, they have contributed to detecting conflicts in collaborative learning through the valence change in dialogues. Finally, the collaboration between the main researcher of the conflict recognition with the other authors started two years ago, working on conflict recognition in learning environments.
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Torres-Jimenez, J., Lescano, G., Lara-Alvarez, C. et al. Conflict recognition in CSCL sessions through the identification of cycles in conversational graphs. Educ Inf Technol 28, 11615–11629 (2023). https://doi.org/10.1007/s10639-022-11576-6
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DOI: https://doi.org/10.1007/s10639-022-11576-6