Utilizing semantic web technologies and data mining techniques to analyze students learning and predict final performance

Abstract

E-learning systems are becoming a fundamental mean of education delivery. Recently, data mining techniques have been utilized by tutors and researchers to analyze students learning with the aim to get deeper sight of it and improve the quality of the educational procedures. In this paper, we present a methodology to analyze students learning and extract semantic rules that can be used to predict student's final performance in the course. Specifically, the students' performance at interim tests during the semester is analyzed and the methodology utilizes decision trees and extracts rules to make predictions regarding the student's final performance in the course. The methodology has been integrated in an educational system used to assist students in learning the Artificial Intelligence (AI) course in our university. The educational system utilizes semantic web technologies such as ontologies and semantic rules to enhance the quality of the educational content and the delivered learning activities to each student. The methodology can assists the system and the tutor to get a deeper insight of the students' performance, trace students that are underachieving or in the edge to fail the final exams and also offer proper recommendations and advises to each one and drive broader pedagogical improvements.