Classification of Learners During an Educational Simulation: Case Study on a Stock Management Simulator
- 507 Downloads
Abstract
This study aims to classify learners into homogeneous groups depending on their traces during an educational simulation using unsupervised learning. Identifying the learner’s group could help the teacher to provide customised advice and thus improve overall learning. This study throws light on the link between the learners’ traces, motivations and performances of learner in a real learning situation. First, we discovered occurrences of a “follower” behaviour among the learners. In further researches, we showed the lack of strong links between several categories of the collected data. This study, realised with the help of 13 teachers on a total of 501 learners following the same course, helped to expose a strong link between students behaviour and the teacher. This study also revealed the benefits of unsupervised learning in order to analyse the learners’ behaviour during the training course. With our results, we will create a new educational simulation with these tools in order to further help teachers understand the behaviour of their students, and improve the quality of teaching.
Keywords
Pattern recognition Educational simulation Unsupervised learning Learner behaviourReferences
- 1.Hallifax, S., Serna, A., Marty, J.C., Lavoué, E.: Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems (ACM 2018), p. LBW073 (2018)Google Scholar
- 2.Brougère, G.: Jouer/apprendre. Economica (2005)Google Scholar
- 3.Alvarez, J.: Du jeu vidéo au serious game: approches culturelle, pragmatique et formelle. Ph.D. thesis, Toulouse 2 (2007)Google Scholar
- 4.Selberg, H., Nielsen, M.E., Horsted, M.W., Bertelsen, K.: Teoksessa Poikela Esa & Tieranta Outi (toim.) Developing Simulation Pedagogy for Nursing Education in an European Network. Rovaniemen AMK, Kopijyvä Oy, Joensuu (2013)Google Scholar
- 5.Hovedskov, J., Selberg, H., Holtzmann, J.: Towards Simulation Pedagogy: Developing Nursing Simulation in a European Network. Rovaniemi University of Applied Sciences (2012)Google Scholar
- 6.Stuart, H., Serna, A., Marty, J.C., Lavoué, E.: Adaptive gamification in education: a literature review of current trends and developments. In: European Conference on Technology Enhanced Learning, pp. 294–307, Springer (2019)Google Scholar
- 7.Covington, M.V.: Making the Grade: A Self-Worth Perspective on Motivation and School Reform. Cambridge University Press, Cambridge (1992)CrossRefGoogle Scholar
- 8.Bartle, R.: J. MUD Res. 1(1), 19 (1996)Google Scholar
- 9.Kolb, D.A.: Experiential Learning: Experience as the Source of Learning and Development. FT Press, Upper Saddle River (2014)Google Scholar
- 10.Sanchez, E.: Usage d’un jeu sérieux dans l’enseignement secondaire. Modélisation comportementale et épistémique de l’apprenant (2011)Google Scholar
- 11.Blikstein, P.: Proceedings of the 1st International Conference on Learning Analytics and Knowledge, pp. 110–116. ACM (2011)Google Scholar
- 12.Serieye, T., Pech-Gourg, N.: 2018 4th International Conference on Logistics Operations Management (GOL), pp. 1–6. IEEE (2018)Google Scholar
- 13.Friedman, J., Shaw, M.E.: The Elements of Statistical Learning. Mentorship in Healthcare, 2nd edn. MK Update Ltd., New York (2014)Google Scholar
- 14.Husson, F., et al.: Analyse de données avec R. Presses Universitaires de Rennes (2017)Google Scholar
- 15.Pedregosa, F., et al.: J. Mach. Learn. Res. 12, 2825 (2011)MathSciNetGoogle Scholar
- 16.van der Maaten, L., Hinton, G.: J. Mach. Learn. Res. 9, 2579–2605 (2008)Google Scholar
- 17.Friedman, J., Hastie, T., Tibshirani, R.: The Elements of Statistical Learning, 2nd edn. Springer, New York (2009)zbMATHGoogle Scholar
- 18.Ding, C., He, X.: Proceedings of the 21st International Machine Learning Conference, pp. 225–232. ACM Press (2004)Google Scholar