Abstract
In Multi-player learning games (MPLG), learners interact with each other through game activities. This paper aims to establish the basis of automatic detection of peer interactions that could emerge from MPLG scenarios. The information provided by this detection could help learning game designers to construct scenarios fostering the interactions they need/desire. We present an algorithm extracting interaction features from MPLG scenarios. Because of the high number of possible sequences of activities in a scenario, we work only on a subset of the sequences which must preserve the information on the interactions emerging from the scenario. We evaluated this algorithm by comparing the calculated subset to the traced game paths obtained from 114 students. The results of this evaluation show that (1) the subset cover well the learners’ traced paths and that (2) the calculated features from the subset have the same types and are distributed similarly to the ones extracted from the traced paths.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Paraskeva, F., Mysirlaki, S., Papagianni, A.: Multiplayer online games as educational tools: facing new challenges in learning. Comput. Educ. 54(2), 498–505 (2010)
Wouters, P., Van Nimwegen, C., Van Oostendorp, H., Van Der Spek, E.D.: A meta-analysis of the cognitive and motivational effects of serious games. J. Educ. Psychol. 105(2), 249 (2013)
Sourmelis, T., Ioannou, A., Zaphiris, P.: Massively multiplayer online role playing games (MMORPGs) and the 21st century skills: a comprehensive research review from 2010 to 2016. Comput. Hum. Behav. 67(2017), 41–48 (2017)
Turkay, S., et al.: Toward understanding the potential of games for learning: learning theory, game design characteristics, and situating video games in classrooms. Comput. Schools 31(1–2), 2–22 (2014)
Eseryel, D., Law, V., Ifenthaler, D., Ge, X., Miller, R.: An investigation of the interrelationships between motivation, engagement, and complex problem solving in game-based learning. Educ. Technol. Soc. 17(1), 42–53 (2014)
Wendel, V., Gutjahr, M., Göbel, S., Steinmetz, R.: Designing collaborative multiplayer serious games. Educ. Inform. Technol. 18(2), 287–308 (2013)
Dyke, G., Girardot, J.J., Lund, K., Corbel, A.: Analysing face to face computer-mediated interactions. In: 12th Biennial International Conference on EARLI (European Association for Research, Learning and Instruction), August 2017
Kumpulainen, K., Mutanen, M.: The situated dynamics of peer group interaction: an introduction to an analytic framework. Learn. Instr. 9(5), 449–473 (1999)
Kreijns, K., Kirschner, P.A., Jochems, W.: Identifying the pitfalls for social interaction in computer-supported collaborative learning environments: a review of the research. Comput. Hum. Behav. 19(3), 335–353 (2003)
Dillenbourg, P.: Introduction; What do you mean by “collaborative learning”? In: Dillenbourg, P. (ed.) Collaborative Learning: Cognitive and Computational Approaches, pp. 1–19 (1999)
Roschelle, J., Teasley, S.D.: The construction of shared knowledge in collaborative problem solving. In: O’Malley, C. (ed.) Computer Supported Collaborative Learning, vol. 128, pp. 69–97. Springer, Berlin (1995). https://doi.org/10.1007/978-3-642-85098-1_5
Kozar, O.: Towards better group work: seeing the difference between cooperation and collaboration. In: English Teaching Forum, vol. 48, no. 2, pp. 16–23 (2010)
Guinebert, M., Yessad, A., Muratet, M., Luengo, V.: An ontology for describing scenarios of multi-players learning games: toward an automatic detection of group interactions. In: Lavoué, É., Drachsler, H., Verbert, K., Broisin, J., Pérez-Sanagustín, M. (eds.) EC-TEL 2017. LNCS, vol. 10474, pp. 410–415. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66610-5_35
Manna, Z., Pnueli, A.: The Temporal Logic of Reactive and Concurrent Systems: Specification. Springer, Heidelberg (2012)
Magee, J., Kramer, J.: State Models and Java Programs. Wiley, Hoboken (1999)
Acknowledgements
We would like to thank the EIAH Chair of Sorbonne-Universités for financing this work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Guinebert, M., Yessad, A., Muratet, M., Luengo, V. (2019). Automatic Detection of Peer Interactions in Multi-player Learning Games. In: Scheffel, M., Broisin, J., Pammer-Schindler, V., Ioannou, A., Schneider, J. (eds) Transforming Learning with Meaningful Technologies. EC-TEL 2019. Lecture Notes in Computer Science(), vol 11722. Springer, Cham. https://doi.org/10.1007/978-3-030-29736-7_26
Download citation
DOI: https://doi.org/10.1007/978-3-030-29736-7_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29735-0
Online ISBN: 978-3-030-29736-7
eBook Packages: Computer ScienceComputer Science (R0)