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.
Highlights
Intelligent Tutoring Systems development began in the1970s with the goal to improve Computer-Assisted Learning (CAL)
STI-driven progress needed to be operationalized through the use of Artificial Intelligence (AI) methods to provide highly personalized feedback-based education tailored to the needs of the students
(i) artificial intelligence explains how to reason about intelligence and about learning, (ii) psychology explains how people think and learn, and (iii) education is about center on the best way to support teaching / learning [2]
Summary
Intelligent Tutoring Systems development began in the1970s with the goal to improve Computer-Assisted Learning (CAL). STI-driven progress needed to be operationalized through the use of Artificial Intelligence (AI) methods to provide highly personalized feedback-based education tailored to the needs of the students. Their aim is to support learning by simulating the teaching skills and field expertise of hu-man tutors and to produce the same kind of learning and flexibility between teachers and students [1]. There are different Intelligent Tutorial Systems with different architectures, their basic architecture has four components (modules / models) that are (see Figure 1): ▪ a Domain-Model that defines the content to be taught; ▪ a Tutoring Model that defines how to teach; ▪ a Student-Model that can personalize the learning taking into account this one; ▪ an Interface-Model that defines the visible means allowing the interrelation be-tween student and the system
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