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Classification of Learners During an Educational Simulation: Case Study on a Stock Management Simulator

  • Denis GuibertEmail author
  • Thibaud Serieye
  • Nicolas Pech-Gourg
Conference paper
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 7)

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 behaviour 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Denis Guibert
    • 1
    • 2
    Email author
  • Thibaud Serieye
    • 2
  • Nicolas Pech-Gourg
    • 2
  1. 1.École Centrale LyonÉcullyFrance
  2. 2.Sciado PartenairesVilleurbanneFrance

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