A Bayesian Network for the Cognitive Diagnosis of Deductive Reasoning

  • Ange Tato
  • Roger Nkambou
  • Janie Brisson
  • Clauvice Kenfack
  • Serge Robert
  • Pamela Kissok
Conference paper

DOI: 10.1007/978-3-319-45153-4_78

Volume 9891 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Tato A., Nkambou R., Brisson J., Kenfack C., Robert S., Kissok P. (2016) A Bayesian Network for the Cognitive Diagnosis of Deductive Reasoning. In: Verbert K., Sharples M., Klobučar T. (eds) Adaptive and Adaptable Learning. EC-TEL 2016. Lecture Notes in Computer Science, vol 9891. Springer, Cham

Abstract

In our previous works, we presented Logic-Muse as an ITS that helps improve logical reasoning skills in multiple contexts. All its three main components (the learner, tutor and expert models) have been developed while relying on the help of experts and on important work in the field of reasoning and computer science. The main purpose of this paper is to present and assess the Bayesian Network (that allows real time diagnosis and modeling of the learner’s state of knowledge) implemented in the learner component. We demonstrate the prediction and the adaptive capabilities for our learner model by using data mining techniques on data from 71 students. We believe this work will help the research community in building and assessing a BN in an ITS that teach logical reasoning.

Keywords

Bayesian network Deductive reasoning Learner model Intelligent tutoring system 

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Ange Tato
    • 1
  • Roger Nkambou
    • 1
  • Janie Brisson
    • 1
  • Clauvice Kenfack
    • 1
  • Serge Robert
    • 1
  • Pamela Kissok
    • 1
  1. 1.Université du Québec à MontréalMontréalCanada