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i-com

Journal of Interactive Media

Editor-in-Chief: Ziegler, Jürgen

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2196-6826
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Volume 17, Issue 1

Issues

Who is the Perfect Match?

Effects of Algorithmic Learning Group Formation Using Personality Traits

Henrik BellhäuserORCID iD: http://orcid.org/0000-0003-4414-7795 / Johannes Konert / Adrienne Müller / René Röpke
Published Online: 2018-03-27 | DOI: https://doi.org/10.1515/icom-2018-0004

Abstract

Using digital tools for teaching allows to unburden teachers from organizational load and even provides qualitative improvements that are not achieved in traditional teaching. Algorithmically supported learning group formation aims at optimizing group composition so that each learner can achieve his or her maximum learning gain and learning groups stay stable and productive. Selecting and weighting relevant criteria for learning group formation is an interdisciplinary challenge. This contribution presents the status quo of algorithmic approaches and respective criteria for learning group formation. Based on this theoretical foundation, we describe an empirical study that investigated the influence of distributing two personality traits (conscientiousness and extraversion) either homogeneously or heterogeneously on subjective and objective measures of productivity, time investment, satisfaction, and performance. Results are compared to an earlier study that also included motivation and prior knowledge as criteria. We find both personality traits to enhance group satisfaction and performance when distributed heterogeneously.

Keywords: Learning Group Formation; CSCL; Homogeneity; Heterogeneity Extraversion; Conscientiousness; Prior Knowledge; Motivation; MoodlePeers

Literature

  • [1]

    Abnar, S., Orooji, F., & Taghiyareh, F. (2012). An evolutionary algorithm for forming mixed groups of learners in web based collaborative learning environments. 2012 IEEE International Conference on Technology Enhanced Education (ICTEE), (pp. 1–6). .CrossrefGoogle Scholar

  • [2]

    Bell, S. T. (2007). Deep-level composition variables as predictors of team performance: a meta-analysis. The Journal of Applied Psychology, 92(3), 595–615. .CrossrefGoogle Scholar

  • [3]

    Bellhäuser, H., Lösch, T., Winter, C., & Schmitz, B. (2016). Applying a web-based training to foster self-regulated learning – Effects of an intervention for large numbers of participants. Internet and Higher Education, 31, 87–100. .CrossrefWeb of ScienceGoogle Scholar

  • [4]

    Cavanaugh, R., & Ellis, M. (2004). Automating the Process of Assigning Students to Cooperative-Learning Teams. Proceedings of the 2004 American Society for Engineering Education Annual Conference & Exposition. Retrieved from http://personal.stevens.edu/~mardis/papers/Cav_Ell_Lay_Ard_asee004.pdf.Google Scholar

  • [5]

    Christodoulopoulos, C. E., & Papanikolaou, K. A. (2007). A Group Formation Tool in an E-Learning Context. 19th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2007), (pp. 117–123). .CrossrefGoogle Scholar

  • [6]

    Gogoulou, A., Gouli, E., Boas, G., Liakou, E., & Grigoriadou, M. (2007). Forming Homogeneous, Heterogeneous and Mixed Groups of Learners. In P. Brusilovsky, M. Grigoriadou, & K. Papanikolaou (Eds), Proceedings of Workshop on Personalisation in E-Learning Environments at Individual and Group Level, 11th International Conference on User Modeling (pp. 33–40).Google Scholar

  • [7]

    Graf, S., & Bekele, R. (2006). Forming heterogeneous groups for intelligent collaborative learning systems with ant colony optimization. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4053 LNCS, (pp. 217–226). .CrossrefGoogle Scholar

  • [8]

    Harbour, R., & Miller, J. (2001). A new system for grading recommendations in evidence based guidelines. British Medical Journal, 323 (August), 334–336. .CrossrefGoogle Scholar

  • [9]

    Harrison, D. A., Price, K. H., Gavin, J. H., & Florey, A. T. (2002). Time, Teams, and Task Performance: Changing Effects of Surface- and Deep-Level Diversity on Group Functioning. Academy of Management Journal, 45(5), 1029–1045. .CrossrefGoogle Scholar

  • [10]

    Henry, T. R. (2013). Creating effective student groups. Proceeding of the 44th ACM Technical Symposium on Computer Science Education – SIGCSE 13, 645. .CrossrefGoogle Scholar

  • [11]

    Hoppe, U. (1995). The Use of Multiple Student Modeling to Parameterize Group Learning. In J. Greer (Ed.), 7th World Conference on Artificial Intelligence in Education (pp. 234–249). AACE: Charlottesville, VA. Retrieved from http://www.collide.info/Members/admin/publications/AIED95_Hoppe.pdf.Google Scholar

  • [12]

    Horwitz, S. K. (2005). The Compositional Impact of Team Diversity on Performance: Theoretical Considerations. Human Resource Development Review, 4(2), 219–245. .CrossrefGoogle Scholar

  • [13]

    Humphrey, S. E., Hollenbeck, J. R., Meyer, C. J., & Ilgen, D. R. (2007). Trait configurations in self-managed teams: a conceptual examination of the use of seeding for maximizing and minimizing trait variance in teams. The Journal of Applied Psychology, 92(3), 885–892. .CrossrefGoogle Scholar

  • [14]

    Inaba, A., Supnithi, T., Ikeda, M., Mizoguchi, R., & Toyoda, J. (2000). How Can We Form Effective Collaborative Learning Groups? In 5th International Conference on ITS (pp. 282–291). Springer: Montreal, Canada. Retrieved from http://www.springerlink.com/index/00XNX7Y4T7UYA1VQ.pdf.Google Scholar

  • [15]

    Konert, J. (2014). Interactive Multimedia Learning: Using Social Media for Peer Education in Single-Player Educational Games. Springer: Heidelberg, Germany. Retrieved from http://www.springer.com/engineering/signals/book/978-3-319-10255-9.Google Scholar

  • [16]

    Konert, J., Bellhäuser, H., Röpke, R., Gallwas, E., & Zucik, A. (2016). MoodlePeers: Factors relevant in learning group formation for improved learning outcomes, satisfaction and commitment in E-learning scenarios using GroupAL. In K. Verbert, M. Sharples, & T. Klobucar (Eds), Adaptive and Adaptable Learning: Proc. of the 11th European Conf. on Techn. Enhanced Learning (EC-TEL 2016) (pp. 390–396). Springer LNCS: Lyon, France. .CrossrefGoogle Scholar

  • [17]

    Konert, J., Burlak, D., & Steinmetz, R. (2014). The Group Formation Problem: An Algorithmic Approach to Learning Group Formation. In C. Rensing, S. de Freitas, T. Ley, & P. J. Muñoz-Merino (Eds), Proceedings of the 9th European Conference on Technology Enhanced Learning (EC-TEL) (pp. 221–234). Springer Berlin: Graz, Austria. .CrossrefGoogle Scholar

  • [18]

    Kyndt, E., Raes, E., Lismont, B., Timmers, F., Cascallar, E., & Dochy, F. (2013). A meta-analysis of the effects of face-to-face cooperative learning. Do recent studies falsify or verify earlier findings? Educational Research Review, 10, 133–149. .CrossrefWeb of ScienceGoogle Scholar

  • [19]

    McCrae, R. R., & Costa, P. T. (1994). The Stability of Personality: Observations and Evaluations. Current Directions in Psychological Science, 3(6), 173–175. .CrossrefGoogle Scholar

  • [20]

    Mitchell, S. N., Reilly, R., Bramwell, F. G., & Lilly, F. (2012). Friendship and Choosing Groupmates: Preferences for Teacher-selected vs. Student-selected Groupings in High School Science Classes. Journal of Instructional Psychology, 31(1), 1–6. Retrieved from http://web.centre.edu/plummer/readings/228readings/mitchell.pdf.Google Scholar

  • [21]

    Nederveen Pieterse, A., van Knippenberg, D., & van Ginkel, W. P. (2011). Diversity in goal orientation, team reflexivity, and team performance. Organizational Behavior and Human Decision Processes, 114(2), 153–164. .CrossrefWeb of ScienceGoogle Scholar

  • [22]

    Ounnas, A., Davis, H., & Millard, D. (2008). A Framework for Semantic Group Formation. Eighth IEEE International Conference on Advanced Learning Technologies, 34–38. .CrossrefGoogle Scholar

  • [23]

    Paredes, P. (2010). A Method for Supporting Heterogeneous-Group Formation through Heuristics and Visualization, 16(19), 2882–2901.

  • [24]

    Rammstedt, B., & John, O. P. (2005). Kurzversion des Big Five Inventory (BFI-K): Entwicklung und Validierung eines ökonomischen Inventars zur Erfassung der fünf Faktoren der Persönlichkeit. Diagnostica, 51(4), 195–206. .CrossrefGoogle Scholar

  • [25]

    Rheinberg, F., Vollmeyer, R., & Burns, B. D. (2001). FAM: Ein Fragebogen zur Erfassung aktueller Motivation QCM: A questionnaire to assess current motivation in learning situations. Diagnostica, (47), 57–66.CrossrefGoogle Scholar

  • [26]

    Richardson, M., Abraham, C., & Bond, R. (2012). Psychological correlates of university students’ academic performance: a systematic review and meta-analysis. Psychological Bulletin, 138(2), 353–387. .CrossrefWeb of ScienceGoogle Scholar

  • [27]

    Röpke, R., Gallwas, E., Konert, J., & Bellhäuser, H. (2016). MoodlePeers: Automatisierte Lerngruppenbildung auf Grundlage psychologischer Merkmalsausprägungen in E-Learning-Systemen. In U. Lucke, A. Schwill, & R. Zender (Eds), Proc. der 14. E-Learning Fachtagung Informatik der g.i. (DeLFI 2016) (pp. 233–244). Köllen Druck+Verlag GmbH.: Bonn.Google Scholar

  • [28]

    Schwarzer, R., & Jerusalem, M. (1999). Skalen zur Erfassung von Lehrer- und Schülermerkmalen. Dokumentation der psychometrischen Verfahren im Rahmen der Wissenschaftlichen Begleitung des Modellversuchs Selbstwirksame Schulen. Verfahren im Rahmen der (Vol. 38).

  • [29]

    Srba, I., & Bielikova, M. (2014b). Dynamic Group Formation as an Approach to Collaborative Learning Support. IEEE Transactions on Learning Technologies, 8(99), 173–186. .CrossrefWeb of ScienceGoogle Scholar

  • [30]

    Wessner, M., & Pfister, H.-R. (2001). Group formation in computer-supported collaborative learning. In Proceedings of the 2001 International ACM SIGGROUP Conference on Supporting Group Work – GROUP’01 (pp. 24–31). .CrossrefGoogle Scholar

  • [31]

    Zheng, Z. (2013). 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).Google Scholar

About the article

Henrik Bellhäuser

Dr Henrik Bellhäuser studied Psychology at the universities of Saarbrücken and Mainz. In his doctoral thesis at Technische Universität Darmstadt, he developed and evaluated a web-based training which aims at fostering self-regulated learning. Currently, he works as a researcher for educational psychology at Johannes Gutenberg-University Mainz and Goethe-University Frankfurt.

Johannes Konert

Johannes Konert is professor for Web Engineering at Beuth University of Applied Sciences in Berlin. With a scholarship of German Research Foundation (DFG) for the research training group “Feedback-based Quality Management in E-learning” he developed solutions to add peer education to single-player educational games. Supplemented with his insights gained from foundation of a social media company, his research focuses on optimization of digital learning experiences.

Adrienne Müller

M. Sc. Adrienne Müller studied Psychology at the university of Mainz. Since October 2017 she works as a researcher for educational psychology at Johannes Gutenberg-University Mainz and started her dissertation focusing on group composition regarding personality traits within the project of Dr Henrik Bellhäuser.

René Röpke

René Röpke studied Computer Science and IT Security at Technische Universität Darmstadt and in his Master’s thesis he developed an identity management solution to support learning analytics in open learning environments. He is currently pursuing his doctorate at RWTH Aachen University in the Learning Technologies Research Group. His interests are among collaborative learning, multi-touch learning applications and computer science education.


Published Online: 2018-03-27

Published in Print: 2018-04-25


Citation Information: i-com, Volume 17, Issue 1, Pages 65–77, ISSN (Online) 2196-6826, ISSN (Print) 1618-162X, DOI: https://doi.org/10.1515/icom-2018-0004.

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