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Students Group Formation Based on Case-Based Reasoning to Support Collaborative Learning

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Konferencja
Federated Conference on Computer Science and Information Systems (15 ; 06-09.09.2020 ; Sofia, Bulgaria)
Języki publikacji
EN
Abstrakty
EN
The group formation has been widely investigated since it is a crucial aspect to perform collaborative work. However, there is no consensus about the best set of metrics or how to combine student's characteristics to improve group interactions, so it has been considered a challenge. Aiming to cope with that, this work proposes the use of case-based reasoning to suggest groups for collaboration based on the metrics and previous groups' performances stored in a case base. We gathered data from students working on collaborative tasks to build a case base and ran a grouping experiment in a class of undergraduates to verify the effectiveness of the proposal. The results evidenced that grouping based on the Big Five improved students' interactions.
Rocznik
Tom
Strony
49--58
Opis fizyczny
Bibliogr. 38 poz., wz., rys.
Twórcy
  • Faculty of Computer Science, Federal University of Uberlândia, João Naves de Ávila 2121, Uberlândia, Brazil
  • Faculty of Computer Science, Federal University of Uberlândia, João Naves de Ávila 2121, Uberlândia, Brazil
Bibliografia
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  • 7. R. C. D. Reis, C. L. Rodriguez, G. C. Challco, P. A. Jaques, I. I. Bittencourt, and S. Isotani, “Relação entre os estados afetivos e as teorias de aprendizagem na formação de grupos em ambientes cscl,” in Anais do XXVI Simpósio Brasileiro de Informática na Educação, 2015. http://dx.doi.org/10.5753/cbie.sbie.2015.1012 pp. 1012–1021.
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  • 11. T. Ferreira, J. Buiar, M. Fernandes, A. Pimentel, and O. Luiz, “Detecção automática de traços de personalidade e recomendação de agrupamento com o modelo big five,” in XXIX Simpósio Brasileiro de Informática na Educação (Brazilian Symposium on Computers in Education), 2018. http://dx.doi.org/10.5753/cbie.sbie.2018.1643 pp. 1643–1652.
  • 12. M. A. G. Peeters, C. G. Rutte, H. F. J. M. van Tuijl, and I. M. M. J. Reymen, “The big five personality traits and individual satisfaction with the team,” Small Group Research, vol. 37(2), p. 187–211, 2006. https://doi.org/10.1177/1046496405285458
  • 13. R. C. D. Reis, S. Isotani, C. L. Rodriguezac, K. T. Lyraa, P. A. Jaques, and I. I. Bittencourte, “Affective states in computer-supported collaborative learning: Studying the past to drive the future,” Computers and Education, vol. 120, pp. 29–50, 2018. https://doi.org/10.1016/j.compedu.2018.01.015
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  • 15. R. R. McCrae and O. John, “An introduction to the five-factor model and its applications,” Journal of Personality, vol. 60, pp. 175–215, 1992. https://doi.org/10.1111/j.1467-6494.1992.tb00970.x
  • 16. H. Spoelstra, P. Van Rosmalen, T. Houtmans, and P. Sloep, “Team formation instruments to enhance learner interactions in open learning environments,” Computers in Human Behavior, vol. 45, pp. 11–20, 2015. https://doi.org/10.1016/j.chb.2014.11.038
  • 17. S. G. B. Roberts, R. Wilson, P. Fedurek, and R. I. M. Dunbar, “Individual differences and personal social network size and structure,” Personality and Individual Differences, vol. 44, pp. 954–964, 2008. https://doi.org/10.1016/j.paid.2007.10.033
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  • 19. T. Ferreira and M. Fernandes, “Detecção de traços de personalidade em textos para apoiar a formação de grupos para colaboração,” in Proceedings of Brazilian Symposium on Computers in Education, 2017. http://dx.doi.org/10.5753/cbie.sbie.2017.1627 pp. 1627–1636.
  • 20. O. C. Santos, A. Rodriguez, E. Gaudioso, and J. G. Boticario, “Helping the tutor to manage a collaborative task in a web-based learning environment,” in Supplementary Proceedings of International Conference on Artificial Intelligence in Education, vol. 4, Sidney, Austrália, 2003, pp. 153–162.
  • 21. R. H. Rutherfoord, “Using personality inventories to form teams for class projects – a case study,” in Proceedings of SIGITE’06 Proceedings of the 7th conference on Information Technology Education. Canterbury, United Kingdom: ACM, 2006. https://doi.org/10.1145/1168812.1168817 pp. 73–76.
  • 22. S. Borges, R. Mizoguchi, I. I. Bittencourt, and S. Isotani, “Group formation in cscl: A review of the state of the art,” Higher Education for All. From Challenges to Novel Technology-Enhanced Solutions, vol. 832, pp. 71–88, 2018. https://doi.org/10.1007/978-3-319-97934-2_5
  • 23. J. L. Kolodner, “An introduction to case-based reasoning,” Artificial Intelligence Review, vol. 6, pp. 3–34, 1992. https://doi.org/10.1007/BF00155578
  • 24. A. Stahl, “Learning of knowledge-intensive similarity measures in case-based reasoning,” Ph.D. dissertation, Departamento de Ciência da Computaçã da Universidade de Kaiserslautern, 10 2003.
  • 25. F. Ricci and P. Avesani, “Learning a local similarity metric for case-based reasoning,” in International Conference on Case-Based Reasoning (ICCBR): Case-Based Reasoning Research and Development, vol. 1010. Sesimbra, Portugal: Springer, 1995. https://doi.org/10.1007/3-540-60598-3_27 pp. 301–312.
  • 26. J. Surma and K. Vanhoof, “Integrating rules and cases for the classification task,” in International Conference on Case-Based Reasoning (ICCBR): Case-Based Reasoning Research and Development, vol. 1010. Berlin, Heidelberg: Springer, 1995. https://doi.org/10.1007/3-540-60598-3_29 pp. 325–334.
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  • 30. J. W. Chang, M. C. Lee, and T. I. Wang, “Integrating a semantic-based retrieval agent into case-based reasoning systems: A case study of an online bookstore,” Computers in Industry, vol. 78, pp. 29–42, 2016. https://doi.org/10.1016/j.compind.2015.10.007 Natural Language Processing and Text Analytics in Industry.
  • 31. S. Begum, M. U. Ahmed, P. Funk, and R. Filla, “Mental state monitoring system for the professional drivers based on heart rate variability analysis and case-based reasoning,” in 2012 Federated Conference on Computer Science and Information Systems (FedCSIS), 2012, pp. 35–42.
  • 32. S. Chen, J. Yi, H. Jiang, and X. Zhu, “Ontology and cbr based automated decision-making method for the disassembly of mechanical products,” Advanced Engineering Informatics, vol. 30, no. 3, pp. 564 – 584, 2016. https://doi.org/10.1016/j.aei.2016.06.005
  • 33. Y. Qin, W. Lu, Q. Qi, X. Liu, M. Huang, P. J. Scott, and X. Jiang, “Towards an ontology-supported case-based reasoning approach for computer-aided tolerance specification,” Knowledge-Based Systems, vol. 141, pp. 129 – 147, 2018. https://doi.org/10.1016/j.knosys.2017.11.013
  • 34. D. Wang, K. Wan, and W. Ma, “Emergency decision-making model of environmental emergencies based on case-based reasoning method,” Journal of Environmental Management, vol. 262, p. 110382, 2020. https://doi.org/10.1016/j.jenvman.2020.110382
  • 35. M. Cocea and G. D. Magoulas, “User behaviour-driven group formation through case-based reasoning and clustering,” Expert Systems with Applications, vol. 39, p. 8756–8768, 2012. https://doi.org/10.1016/j.eswa.2012.01.205
  • 36. R. Costaguta, “Algorithms and machine learning techniques in collaborative group formation,” in MICAI 2015: Advances in Artificial Intelligence and Its Applications, 2015. https://doi.org/10.1007/978-3-319-27101-9_18 pp. 249–258.
  • 37. O. John and S. Srivastava, “The Big Five trait taxonomy: History, measurement, and theoretical perspectives,” Handbook of personality: Theory and research, vol. 2, pp. 102–138, 1999.
  • 38. J. M. Andrade, “Evidências de validade do inventário dos cinco grandes fatores de personalidade para o brasil,” Ph.D. dissertation, Instituto de Psicologia - Universidade de Brasília, 7 2008.
Uwagi
1. Track 1: Artificial Intelligence
2. Technical Session: 15th International Symposium Advances in Artificial Intelligence and Applications
3. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-76321ded-4b13-4266-b714-ab7f88562ea5
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