Advertisement

Peer Group Formation for Learning

  • Johannes KonertEmail author
Chapter
  • 1.3k Downloads
Part of the Springer Theses book series (Springer Theses)

Abstract

To improve the effectiveness of group learning in general, and to address in particular opportunities for educational games, this chapter introduces a technique to match users by considering homogeneous and heterogeneous criteria for matching. First, the chapter identifies the requirements for the algorithmic design, based on the findings from related work analysis and the Group Formation Problem (Sect.  2.1.3). Second, the modeling of matching criteria is derived (Sect. 6.1) and metrics for the group formation algorithm are developed (Sect. 6.2). This includes the proposal of three matching algorithms, and the metrics of GPI and CPI, which fulfill the defined requirements. Finally, the approach towards optimization is discussed and algorithms are designed to optimize the cohorts and handle incremental updates. Details about the corresponding conceptualized API methods can be found in Sect. A.1.

References

  1. 1.
    Johannes Konert, Dmitrij Burlak, Stefan Gobel, and Ralf Steinmetz. GroupAL: ein Algorithmus zur Formation und Qualitatsbewertung von Lerngruppen in ELearning- Szenarien mittels n-dimensionaler Gutekriterien. In Andreas Breitner and Christoph Rensing, editors, Proceedings of the DeLFI 2013: Die 11. e-Learning Fachtagung Informatik der Gesellschaft fur Informatik e.V., pages 71–82, Bremen, Germany, 2013. Kollen. ISBN 9783885796121.Google Scholar
  2. 2.
    Dmitrij Burlak. Analyse, Design und Implementierung von algorithmenbasierter Lerngruppen-Optimierung. Master thesis (department type number kom-d-0462), Technische Universitat Darmstadt, 2013.Google Scholar
  3. 3.
    William Damon. Peer Education: The Untapped Potential. Journal of Applied Developmental Psychology, 5(4):331–343, December 1984. ISSN 01933973.Google Scholar
  4. 4.
    Alon Lisak and Miriam Erez. Leaders and followers in multi-cultural teams. In Proceeding of the 2009 international workshop on Intercultural collaboration - IWIC ’09, page 81, New York, New York, USA, February 2009. ACM Press. ISBN 9781605585024.Google Scholar
  5. 5.
    Kyong Jin Shim and Jaideep Srivastava. Team Performance Prediction in Massively Multiplayer Online Role-Playing Games (MMORPGs). 2010 IEEE Second International Conference on Social Computing, pages 128–136, August 2010.Google Scholar
  6. 6.
    B Barry and G L Stewart. Composition, Process, and Performance in Self- Managed Groups: the Role of Personality. The Journal of Applied Psychology, 82 (1):62–78, February 1997. ISSN 0021–9010.Google Scholar
  7. 7.
    Asma Ounnas, David E. Millard, and Hugh C. Davis. A Metrics Framework for Evaluating Group Formation. Proceedings of the 2007 international ACM Conference on Supporting Group Work - GROUP ’07, page 221, 2007.Google Scholar
  8. 8.
    Ryan Cavanaugh and Matt Ellis. Automating the Process of Assigning Students to Cooperative-Learning Teams. Proceedings of the 2004 American Society for Engineering Education Annual Conference & Exposition, 2004.Google Scholar
  9. 9.
    Asma Ounnas, Hugh Davis, and David Millard. A Framework for Semantic Group Formation. Eighth IEEE International Conference on Advanced Learning Technologies, pages 34–38, 2008.Google Scholar
  10. 10.
    Pedro Paredes, Alvaro Ortigosa, and Pilar Rodriguez. A Method for Supporting Heterogeneous-Group Formation through Heuristics and Visualization. Journal of Universal Computer Science, 16(19):2882–2901, 2010.Google Scholar
  11. 11.
    Jorg Haake, Gerhard Schwabe, and Martin Wessner. CSCL-Kompendium: Lehrund Handbuch zum Computeruntersutzten Kooperativen Lernen. Oldenbourg Verlag, 2004. ISBN 3486274368.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Electrical Engineering and Information TechnologyTechnische Universität DarmstadtDarmstadtGermany

Personalised recommendations