Multi-Relational and Social-Influence Model for Predicting Student Performance in Intelligent Tutoring Systems (ITS)
Kouamé Abel Assielou1, Cissé Théodore Haba2, Tanon Lambert Kadjo3, Kouakou Daniel Yao4, Bi Tra Goore5
1Kouamé Abel Assielou*, Laboratoire de Recherche, Informatique Télécommunication (LARIT), Institut National Polytechnique Felix Houphouet Boigny (INP-HB), Yamoussoukro, Côte d’Ivoire.
2Cissé Théodore Haba, Department of Training and Research of Electrical & Electronics Engineering, Institut National Polytechnique Felix Houphouet Boigny (INP-HB), Yamoussoukro, Côte d’Ivoire.
3Tanon Lambert Kadjo, Laboratoire De Recherche en Informatique Et Télécommunication (LARIT), Institut National Polytechnique Felix Houphouet Boigny (INP-HB), Yamoussoukro, Côte d’Ivoire.
4Kouakou Daniel Yao, Laboratory of studies and prevention in Psychoeducation (LEPPE-ENS), University Jean Lorougnon Guédé, Daloa, Côte d’Ivoire.
5Bi Tra Goore, Laboratoire de Recherche en Informatique et Télécommunication (LARIT), Institut National Polytechnique Felix Houphouet Boigny (INP-HB), Yamoussoukro, Côte d’Ivoire.
Manuscript received on January 08, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 2058-2066 | Volume-9 Issue-3, February 2020. | Retrieval Number: C5169029320/2020©BEIESP | DOI: 10.35940/ijeat.C5169.029320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Matrix Factorization, Student Performance Prediction, Intelligent Tutoring System, Social-Influence, Recommender Systems.