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Markov Decision Process for MOOC Users Behavioral Inference

  • Firas JarbouiEmail author
  • Célya Gruson-Daniel
  • Alain Durmus
  • Vincent Rocchisani
  • Sophie-Helene Goulet Ebongue
  • Anneliese Depoux
  • Wilfried Kirschenmann
  • Vianney Perchet
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11475)

Abstract

Studies on massive open online courses (MOOCs) users discuss the existence of typical profiles and their impact on the learning process of the students. However defining the typical behaviors as well as classifying the users accordingly is a difficult task. In this paper we suggest two methods to model MOOC users behaviour given their log data. We mold their behavior into a Markov Decision Process framework. We associate the user’s intentions with the MDP reward and argue that this allows us to classify them.

Keywords

User behaviour studies Learning analytics Markov Decision Process Inverse Reinforcement Learning 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Firas Jarboui
    • 1
    • 2
    Email author
  • Célya Gruson-Daniel
    • 3
    • 5
  • Alain Durmus
    • 2
  • Vincent Rocchisani
    • 1
  • Sophie-Helene Goulet Ebongue
    • 3
  • Anneliese Depoux
    • 3
    • 4
  • Wilfried Kirschenmann
    • 1
  • Vianney Perchet
    • 2
  1. 1.ANEOBoulogne BillancourtFrance
  2. 2.CMLA, École normale supérieur Paris SaclayUniversité Paris SaclayParisFrance
  3. 3.Centre Virchow-Villermé for Public Health Paris-BerlinUniversité Sorbonne Paris-CitéParisFrance
  4. 4.GRIPIC - EA 1498Sorbonne UniversitéParisFrance
  5. 5.DRISS (Digital Research in Science & Society)ParisFrance

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