Published February 29, 2020 | Version v1
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Multi-Relational and Social-Influence Model for Predicting Student Performance in Intelligent Tutoring Systems (ITS)

  • 1. Laboratoire de Recherche en Informatique et Télécommunication (LARIT), Institut National Polytechnique Felix HOUPHOUET Boigny (INP-HB), Yamoussoukro, Côte d'Ivoire.
  • 2. Department of Training and Research of Electrical & Electronics Engineering, Institut National Polytechnique Felix HOUPHOUET Boigny (INP-HB), Yamoussoukro, Côte d'Ivoire
  • 3. Laboratory of studies and prevention in Psychoeducation (LEPPE-ENS), University Jean Lorougnon Guédé, Daloa, Côte d'Ivoire
  • 1. Publisher

Description

Recent studies have shown that Matrix Factorization (MF) method, deriving from recommendation systems, can predict student performance as part of Intelligent Tutoring Systems (ITS). In order to improve the accuracy of this method, we hypothesize that taking into account the mutual influence effect in the relations of student groups would be a major asset. This criterion, coupled with those of the different relationships between the students, the tasks and the skills, would thus be essential elements for a better performance prediction in order to make personalized recommendations in the ITS. This paper proposes an approach for Predicting Student Performance (PSP) that integrates not only friendship relationships such as workgroup relationships, but also mutual influence values into the Weighted Multi-Relational Matrix Factorization method. By applying the Root Mean Squared Error (RMSE) metric to our model, experimental results from KDD Challenge 2010 database show that this approach allows to refine student performance prediction accuracy.

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Journal article: 2249-8958 (ISSN)

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ISSN
2249-8958
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C5169029320/2020©BEIESP