Bayesian networks for detection of teams roles in learning collaborative supported by computers
DOI:
https://doi.org/10.25044/25392190.476Keywords:
team roles, computer supported collaborative learning, Bayesian networks.Abstract
Computer-supported collaborative learning allows students who are in different places to work together in the same virtual space, and supports the communication of ideas and information among learners. However, as not all students are identical, it is important to study users' characteristics to build more productive teams. Team Roles Theory allows obtaining very good team performance taking into account individual skills, combining the weaknesses of each role with the strengths of others. Originally, people have to complete extensive questionnaires to determine their team role. In this work we propose an alternative method to make this detection through a collaborative learning system and by using a Bayesian Network.Downloads
References
Aguilar, R., de Antonio, A., & Imbert, R. (2007) Searching Pancho's soul: An intelligent virtual agent for human teams. In Proceedings of CERMA'07, 568–571, Washington, DC, USA.
Aritzeta, A., Swailes, S., & Senior, B. (2007) Belbin's team role model: Development, validity and applications for team building. Journal of Management Studies, 44 (1): 96–11.
Bales, R., & Strodtbeck, F. (1951). Phases in group problem-solving. Journal of Abnormal and Social Psychology, 46 (4): 485–495.
Belbin, R. (1981). Management teams: Why they succeed or fail. Oxford: Butterwoth-Heinemann.
Belbin, R. (1993) Team roles at work. Oxford: Butterwoth-Heinemann.
Costaguta, R, Schiaffino, S., & Fares, R. (2012) Fostering team-role balance in Computer Supported Collaborative Learning, New Horizons in Creative Open Software, Multimedia, Human Factors and Software Engineering, (pp. 82–90).
Blue Herons. da Silva, F. & Cesar, A. (2009). An experimental research on the relationships between preferences for technical activities and behavioural profile in software development. In Proceedings of the SBES '09, (pp. 126–135).
Dafoulas, G. & Macaulay, L. (2001). Facilitating group formation and role allocation in software engineering groups. In Proceedings of the ACS/IEEE International Conference on Computer Systems and Applications (AICCSA '01), (pp. 352–359), Beirut, Libano.
Jensen, F. (1996) Introduction to Bayesian Networks. Springer-Verlag.
Licorish, S., Philpott, A., & MacDonell, S. G. (2009)
Supporting agile team composition: A prototype tool for identifying personality (in)compatibilities. In Proceedings of the 2009 ICSE Workshop CHASE '09, (pp. 66–73), Vancouver, Canada.
Mumma, F. (1992) Team-Work & Team-Roles. King of Prussia: HRDQ.
Mumma, F. (1994) What makes your team tick? HRDQ.
Ou, K., Wang, C. & Chen, G. (2005) Identify group roles by text mining on group discussion in a Web-based learning system. In Proceedings of 2005 International Conference Machine Learning and Cybernetics (ICMLC'2005), (pp. 5566–5572), Guangzhou, China
Pearl, J. (1988) Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann Publishers Inc..
Pohl, W., Kobsa, A., & Kutter, O. (1995) User model acquisition heuristics based on dialogue acts. In International Conference on the Design of Cooperative Systems, (pp. 471–486).
Wang, C., Ou, K., Liu, C., Liu, B., & Chen, G. (2002). Using a text miner to identify group roles of students in a Web-based learning system. International Journal of Learning, 9:1033–1043.
Winter, M. F. (2004) Developing a group model for student software engineer- ing teams. PhD thesis.
Zancanaro, M., Lepri, B. & Pianesi, F. (2006) Automatic detection of group functional roles in face to face interactions. In Proceedings of the 8th International Conference on Multimodal Interfaces (ICMI '06), pages