Open AccessMulti Relational and Social Influence Model for Predicting Student Performance in Intelligent Tutoring Systems ITS
Author(s) -
Kouamé Abel Assielou,
Cissé Théodore Haba,
Ta Lambert Kadjo,
Kouakou Daniel Yao,
Bi Tra Gooré
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.c5169.029320
Subject(s) - workgroup , computer science , metric (unit) , friendship , machine learning , artificial intelligence , mean squared error , recommender system , data mining , order (exchange) , mathematics , statistics , psychology , computer network , social psychology , operations management , finance , economics
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.