MoodlePeers: Factors Relevant in Learning Group Formation for Improved Learning Outcomes, Satisfaction and Commitment in E-Learning Scenarios Using GroupAL
High-scale and pure online learning scenarios (like MOOCs) as well as blended-learning scenarios offer great possibilities to optimize the composition of learning groups working together on the assigned (or selected) tasks. While the benefits and importance of peer learning for deep learning and improvement of e.g. problem-solving competency and social skills are indisputable, little evidences exist about the relevant factors for group formation and their combination to optimize the learning outcome for all participants (in all groups). Based on the GroupAL algorithm, MoodlePeers proposes an plugin solution for Moodle. Evaluated in a four-week online university mathematics preparation course MoodlePeers proved significant differences in submission rate of homework, quality of homework, keeping up, and satisfaction with group work compared to randomly created groups. The significant factors from personality traits, motivation and team orientation are discussed as well as the algorithmic key functionality behind.
KeywordsGroup formation Learning outcome Learning goal alignment Peer learning Personality traits Motivation Expectation Optimization
This work has partly been funded by the TU Darmstadt Quality Program for excellent teaching. Additionally, special thanks to the co-workers on this interdisciplinary work, especially Regina Bruder, Annette Glathe, Christian Hoppe, Steffen Pegenau, Christoph Rensing, Diana Seyfarth, Marcel Schaub, Klaus Steitz, and Nora Wester (all TU Darmstadt).
- 2.Michaelsen, L.K., Fink, L.D., Hall, A.: Designing effective group activities: lessons for classroom teaching and faculty development. In: DeZure, D. (ed.) To Improve the Academy: Resources for Faculty, Instructional and Organizational Development. New Forums, Stollwater, OK (1997)Google Scholar
- 9.Konert, J., Burlak, D., Steinmetz, R.: The group formation problem: an algorithmic approach to learning group formation. In: Rensing, C., de Freitas, S., Ley, T., Muñoz-Merino, P.J. (eds.) Proceedings of the 9th European Conference on Technology Enhanced Learning (EC-TEL), pp. 221–234. Springer, Berlin, Graz, Austria (2014)Google Scholar
- 10.Zheng, Z.: A dynamic group composition method to refine collaborative learning group formation. In: Proceedings of the 6th International Conference on Educational Data Mining (EDM). pp. 360–361 (2013)Google Scholar
- 11.Cavanaugh, R., Ellis, M.: Automating the process of assigning students to cooperative-learning teams. In: Proceedings 2004 American Society for Engineering Education Annual Conference & Exposition (2004)Google Scholar
- 14.Hosiep, R., Paschen, M.: Das Bochumer Inventar zur berufsbezogenen Persönlichkeitsbeschreibung - 6 Faktoren. Hogrefe, Göttingen (2012)Google Scholar