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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Discover the latest articles, news and stories from top researchers in related subjects.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.
Notes
pySentimiento - https//github.com/finiteautomata/pysentimiento/
References
Agarwal, B., & Mittal, N. (2014). Semantic feature clustering for sentiment analysis of english reviews. IETE Journal of Research, 60(6), 414–422. https://doi.org/10.1080/03772063.2014.963172.
Ayoko, O. B., Callan, V. J., & Härtel, C. E. J. (2008). The influence of team emotional intelligence climate on conflict and team members’ reactions to conflict. Small Group Research, 39(2), 121–149. https://doi.org/10.1177/1046496407304921.
Bales, R. (1950). Interaction process analysis: A method for the study of small groups. Cambridge, Mass: Addison-Wesley Press.
Bales, R., & Steven, C. (1979). SYMLOG: A system for the multiple level observation of groups. New York: Free.
Barki, H., & Hartwick, J. (2004). Conceptualizing the construct of interpersonal conflict. International Journal of Conflict Management.
Barsade, S. G. (2009). The ripple effect: Emotional contagion and its influence on group behavior. Administrative Science Quarterly, 47(4), 644–675. https://doi.org/10.2307/3094912.
Bender, M. A., Fineman, J. T., Gilbert, S., & et al. (2015). A new approach to incremental cycle detection and related problems. ACM Transactions on Algorithms (TALG), 12(2), 1–22. https://doi.org/10.1145/2756553.
Berdun, F. D., Armentano, M. G., Berdun, L., & et al. (2018). Classification of collaborative behavior from free text interactions. Computers & Electrical Engineering, 65, 428–437. https://doi.org/10.1016/j.compeleceng.2017.07.015.
Bhattacharya, S., & Kulkarni, J. (2020). An improved algorithm for incremental cycle detection and topological ordering in sparse graphs. In Proceedings of the 14th annual ACM-SIAM symposium on discrete algorithms (pp. 2509–2521). SIAM.
Cambria, E., Speer, R., Havasi, C., & et, al (2010). Senticnet: A publicly available semantic resource for opinion mining. In AAAI fall symposium: Commonsense knowledge. Citeseer.
Curşeu, P. L., Boroş, S., & Oerlemans, L. A. (2012). Task and relationship conflict in short-term and long-term groups: The critical role of emotion regulation. International Journal of Conflict Management.
Deng, X., Li, Y., Weng, J., & et al. (2019). Feature selection for text classification: A review. Multimedia Tools & Applications, 78(3). https://doi.org/10.1007/s11042-018-6083-5.
Dreu, C. K. W. D., & Weingart, L. R. (2003). Task versus relationship conflict, team performance, and team member satisfaction: A meta-analysis. Journal of Applied Psychology, 88(4), 741–749. https://doi.org/10.1037/0021-9010.88.4.741.
Garcia-Prieto, P., Mackie, D. M., Tran, V., & et al. (2007). Chapter 7 intergroup emotions in workgroups: Some emotional antecedents and consequences of belonging. Research on Managing Groups and Teams, 10, 145–184. https://doi.org/10.1016/S1534-0856(07)10007-4.
Haddi, E., Liu, X., & Shi, Y. (2013). The role of text pre-processing in sentiment analysis. Procedia Computer Science, 17, 26–32. https://doi.org/10.1016/j.procs.2013.05.005.
Haeupler, B., Kavitha, T., Mathew, R., & et al. (2012). Incremental cycle detection, topological ordering, and strong component maintenance. ACM Transactions on Algorithms (TALG), 8(1), 1–33. https://doi.org/10.1145/2071379.2071382.
Jehn, K. A. (1997). A qualitative analysis of conflict types and dimensions in organizational groups. Administrative Science Quarterly, 42(3), 530–557. https://doi.org/10.2307/2393737.
Jiang, J. Y., Zhang, X., & Tjosvold, D. (2013). Emotion regulation as a boundary condition of the relationship between team conflict and performance: A multi-level examination. Journal of Organizational Behavior, 34(5), 714–734. https://doi.org/10.1002/job.1834.
Krippendorff, K. (2004). Content analysis: An introduction to its methodology. USA: SAGE Publications.
Lee, D., Huh, Y., & Reigeluth, C. M. (2015). Collaboration, intragroup conflict, and social skills in project-based learning. Instructional Science, 43(5), 561–590. https://doi.org/10.1007/s11251-015-9348-7.
Lescano, G., & Costaguta, R. (2018). COLLAB: Conflicts and sentiments in chats. In XIX international conference on human computer interaction (Interacción 2018), DOI https://doi.org/10.1145/3233824.3233864.
Lescano, G., Lara, C., Collazos, C., & et al (2020). A technique for conflict detection in collaborative learning environment by using text sentiment. In L. Martínez-Villaseñor, O. Herrera Alcántara, H. Ponce, & et al. (Eds.) Advances in computational intelligence, (II), lncs. lnai edn. https://doi.org/10.1007/978-3-030-60887-3_4, (Vol. 12469 pp. 39–50). Cham: Springer Nature Switzerland AG.
Lescano, G., Torres-Jimenez, J., Costaguta, R., & et al. (2021). Detecting conflicts in collaborative learning through the valence change of atomic interactions. Expert Systems with Applications, 183(2020). https://doi.org/10.1016/j.eswa.2021.115291.
Li, Y., Gao, J., Li, Q., & et al. (2015). Ensemble learning. In C Aggarwal (Ed.) Data classification. Algorithms and applications (p. 483). New York: Chapman & Hall/CRC.
Lipponen, L. (2001). Supporting collaboration with computers. In M. Lakkala, M. Rahikainen, & K Hakkarainen (Eds.) D2.1 perspectives of CSCL in EuropeL A review. ITCOLE Project (pp. 7–12).
Loh, C. Y. R., & Teo, T. C. (2016). Students’ perception of collaborative learning, conflict management and satisfaction in a private educational institution learning environment: An Asian Case Study. Journal of Education & Social Policy, 3(3), 72–79. https://doi.org/10.30845/jesp.
Luque, F. M., & Pérez, J M (2018). Atalaya at TASS 2018: Sentiment analysis with tweet embeddings and data augmentation. In E. Martínez Cámara, Y. Almeida-Cruz, M.C. Díaz Galiano, & et al. (Eds.) Proceedings of TASS 2018: Workshop on sentiment analysis at SEPLN. CEUR-WS, Sevilla, España, (Vol. 2172 pp. 29–35).
Martínez Cámara, E., Almeida-Cruz, Y., Díaz Galiano, M. C., et al., & et al. (2018). Overview of TASS 2018: Opinions, health and emotions. In E. Martínez Cámara, Y. Almeida-Cruz, & M.C. Díaz Galiano (Eds.) Proceedings of TASS 2018: workshop on semantic analysis at SEPLN (TASS 2018). CEUR-WS, Sevilla, España.
Millar, F. E., Rogers, L. E., & Bavelas, J. B. (1984). Identifying patterns of verbal conflict in interpersonal dynamics. Western Journal of Speech Communication, 48(3), 231–246. https://doi.org/10.1080/10570318409374159.
Monteserin, A., Schiaffino, S., & Amandi, A. (2010). Assisting students with argumentation plans when solving problems in CSCL. Computers and Education, 54(2), 416–426. https://doi.org/10.1016/j.compedu.2009.08.025.
Näykki, P, Järvelä, S, Kirschner, P. A., & et al. (2014). Socio-emotional conflict in collaborative learning-A process-oriented case study in a higher education context. International Journal of Educational Research, 68(2014), 1–14. https://doi.org/10.1016/j.ijer.2014.07.001.
Pekrun, R. (2014). Emotions and learning. Practices Series-24. International Academy of Education & International Bureau of Education.
Rao, A. S, Georgeff, M. P., & et al. (1995). BDI agents: From theory to practice. In Icmas (pp. 312–319).
Russell, J. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39, 1161–1178. https://doi.org/10.1037/h0077714.
Tedesco, P. A. (2003). MArCo: Building an artificial conflict mediator to support group planning interactions. International Journal of Artificial Intelligence in Education, 13(1), 117–155.
Verbiest, N., Vermeulen, K., & Teredesai, A. (2015). Evaluation of classification methods. In C. Aggarwal (Ed.) Data classification. Algorithms and applications (pp. 633–656). New York: Chapman & Hall/CRC.
Wall, V. D., & Galanes, G. J. (1986). The SYMLOG dimensions and small group conflict. Central States Speech Journal, 37(2), 61–78. https://doi.org/10.1080/10510978609368206.
Yousefpour, A., Ibrahim, R., & Hamed, H. N. A. (2017). Ordinal-based and frequency-based integration of feature selection methods for sentiment analysis. Expert Systems with Applications, 75, 80–93. https://doi.org/10.1016/j.eswa.2017.01.009.
Zachary, W. W. (1977). An information flow model for conflict and fission in small groups. Journal of Anthropological Research, 33(4), 452–473.
Zakaria, C., Curé, O., & Smaïli, K. (2009). Conflict ontology enrichment based on triggers. In The 2nd international workshop on ontologies and information systems for the semantic Napa Valley, California, United States. https://doi.org/10.1145/1458484.1458501.
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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