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Group Formation Algorithms in Collaborative Learning Contexts: A Systematic Mapping of the Literature

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Collaboration and Technology (CRIWG 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8658))

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Abstract

Group Formation is a complex and important step to design effective collaborative learning activities. Through the adequate selection of individuals to a group, it is possible to create environments that foster the occurrence of meaningful interactions, and thereby, increasing robust learning and intellectual growth. Many researchers indicate that the inadequate formation of groups can demotivate students and hinder the learning process. Thus, in the field of Computer-Supported Collaborative Learning (CSCL), there are several studies focusing on developing and testing group formation in collaborative learning contexts using best practices and other pedagogical approaches. Nevertheless, the CSCL community lacks a comprehensive understanding on which computational techniques (i.e. algorithms) has supported group formation. To the best of our knowledge, there is no study aimed at gathering and analyzing the research findings on this topic using a systematic method. To fill this gap, this research conducted a systematic mapping with the objective of summarizing the studies on algorithms for group formation in CSCL contexts. Initially, by searching on six digital libraries, we collected 256 studies. Then, after a careful analysis of each study, we verified that only 48 were related to group formation applied to collaborative learning contexts. Finally, we categorized the contributions of these studies to present an overview of the findings produced by the community. This overview shows that: (i) there is a gradual increase on research published in this topic; (ii) 41% of the algorithms for group formation area based on probabilistic models; (iii) most studies presented the evaluation of tools that implement these algorithms; but (iv) only 2% of the studies provide their source code; and finally, (v) there is no tool or guideline to compare the benefits, differences and specificities of group formation algorithms available to date. As a result of this work an infographic is also available at: http://infografico.caed-lab.com/mapping/gf.

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Authors and Affiliations

  1. Department of Computer Systems, University of Sao Paulo, Sao Carlos, Brazil

    Wilmax Marreiro Cruz & Seiji Isotani

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  1. Wilmax Marreiro Cruz
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  2. Seiji Isotani
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Editors and Affiliations

  1. Department of Computer Science, Universidad de Chile, Blanco Encalada, 2120, Santiago, Chile

    Nelson Baloian

  2. Faculty of Information Technology, Monash University, Caulfield Campus, 3145, Melbourne, VIC, Australia

    Frada Burstein

  3. Graduate School of Information Science and Electrical Engineering, Kyushu University, 744, Motooa, 819-0395, Nhishi-ku, Fukuoka, Japan

    Hiroaki Ogata

  4. Centro de Ciências Exatas e da Terra, Departamento de Informática Aplicada, Universidade Federal do Estado, Avenida Pasteur, 758, Urca, 22245-040, Rio lde Janeiro, RJ, Brazil

    Flavia Santoro

  5. Diagonal Paraquay 257, 8330015, Santiago, Chile

    Gustavo Zurita

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Cruz, W.M., Isotani, S. (2014). Group Formation Algorithms in Collaborative Learning Contexts: A Systematic Mapping of the Literature. In: Baloian, N., Burstein, F., Ogata, H., Santoro, F., Zurita, G. (eds) Collaboration and Technology. CRIWG 2014. Lecture Notes in Computer Science, vol 8658. Springer, Cham. https://doi.org/10.1007/978-3-319-10166-8_18

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  • DOI: https://doi.org/10.1007/978-3-319-10166-8_18

  • Publisher Name: Springer, Cham

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