MoodlePeers: Factors Relevant in Learning Group Formation for Improved Learning Outcomes, Satisfaction and Commitment in E-Learning Scenarios Using GroupAL

  • Johannes KonertEmail author
  • Henrik Bellhäuser
  • René Röpke
  • Eduard Gallwas
  • Ahmed Zucik
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9891)


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.


Group 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).


  1. 1.
    Damon, W.: Peer education: the untapped potential. J. Appl. Dev. Psychol. 5, 331–343 (1984)CrossRefGoogle Scholar
  2. 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
  3. 3.
    Srba, I., Bielikova, M.: Dynamic Group formation as an approach to collaborative learning support. IEEE Trans. Learn. Technol. 8(2), 173–186 (2014)CrossRefGoogle Scholar
  4. 4.
    Harrison, D.A., Price, K.H., Gavin, J.H., Florey, A.T.: Time, teams, and task performance: changing effects of surface- and deep-level diversity on group functioning. Acad. Manag. J. 45, 1029–1045 (2002)CrossRefGoogle Scholar
  5. 5.
    Humphrey, S.E., Hollenbeck, J.R., Meyer, C.J., Ilgen, D.R.: Trait configurations in self-managed teams: a conceptual examination of the use of seeding for maximizing and minimizing trait variance in teams. J. Appl. Psychol. 92, 885–892 (2007)CrossRefGoogle Scholar
  6. 6.
    Bell, S.T.: Deep-level composition variables as predictors of team performance: a meta-analysis. J. Appl. Psychol. 92, 595–615 (2007)CrossRefGoogle Scholar
  7. 7.
    Nederveen Pieterse, A., van Knippenberg, D., van Ginkel, W.P.: Diversity in goal orientation, team reflexivity, and team performance. Organ. Behav. Hum. Decis. Process. 114, 153–164 (2011)CrossRefGoogle Scholar
  8. 8.
    Horwitz, S.K.: The compositional impact of team diversity on performance: theoretical considerations. Hum. Resour. Dev. Rev. 4, 219–245 (2005)CrossRefGoogle Scholar
  9. 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. 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. 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
  12. 12.
    Isotani, S., Mizoguchi, R.: Theory-driven group formation through ontologies. Int. J. Comput. Collab. Learn. 4, 445–478 (2009)CrossRefGoogle Scholar
  13. 13.
    Rammstedt, B., John, O.P.: Kurzversion des big five inventory (BFI-K): entwicklung und validierung eines ökonomischen inventars zur erfassung der fünf faktoren der persönlichkeit. Diagnostica 51, 195–206 (2005)CrossRefGoogle Scholar
  14. 14.
    Hosiep, R., Paschen, M.: Das Bochumer Inventar zur berufsbezogenen Persönlichkeitsbeschreibung - 6 Faktoren. Hogrefe, Göttingen (2012)Google Scholar
  15. 15.
    Rheinberg, F., Vollmeyer, R., Burns, B.D.: FAM: Ein Fragebogen zur Erfassung aktueller Motivation QCM: A questionnaire to assess current motivation in learning situations. Diagnostica 47, 57–66 (2001)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Johannes Konert
    • 1
    Email author
  • Henrik Bellhäuser
    • 2
  • René Röpke
    • 3
  • Eduard Gallwas
    • 3
  • Ahmed Zucik
    • 3
  1. 1.Beuth University of Applied Sciences BerlinBerlinGermany
  2. 2.Department of PsychologyUniversity of MainzMainzGermany
  3. 3.TU DarmstadtDarmstadtGermany

Personalised recommendations