Who is the Perfect Match?

Effects of Algorithmic Learning Group Formation Using Personality Traits

  • 1 Universität Mainz, Binger Str. 14-16, Mainz, Germany
  • 2 Beuth Hochschule für Technik, Luxemburger Str. 10, Berlin, Germany
  • 3 RWTH Aachen University, Ahornstraße 55, Aachen, Germany
Henrik BellhäuserORCID iD: http://orcid.org/0000-0003-4414-7795
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  • Universität Mainz, Psychologie in den Bildungswissenschaften, Binger Str. 14-16, 55099, Mainz, Germany
  • orcid.org/0000-0003-4414-7795
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  • 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.
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, Johannes Konert
  • 38963Beuth Hochschule für Technik, FB VI Informatik und Medien, Luxemburger Str. 10, 13353, Berlin, Germany
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  • 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.
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, Adrienne Müller
  • Universität Mainz, Psychologie in den Bildungswissenschaften, Binger Str. 14-16, 55099, Mainz, Germany
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  • 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.
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and René Röpke
  • RWTH Aachen University, Informatik 9 (Learning Technologies), Ahornstraße 55, 52074, Aachen, Germany
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  • 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.
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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.

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