An Hybrid Ontology Matching Mechanism for Adaptive Educational eLearning Environments

Abstract

Providing the same pedagogical and educational methods to all students is pedagogically ineffective. In contrast, the pedagogical strategies that adapt to the fundamental individual skills of the students have proved to be more effective. An important innovation in this direction is the adaptive educational systems (AESs) that adjust the teaching content on educational needs and students’ skills. Effective utilization of these approaches can be enhanced with artificial intelligence (AI) and semantic web technologies that can increase data generation, access, flow, integration, and comprehension using the same open standards driving the World Wide Web. This study proposes a novel adaptive educational eLearning system (AEeLS) that can gather and analyze data from learning repositories and adapt these to the educational curriculum according to the student’s skills and experience. It is an innovative hybrid machine learning system that combines a semi-supervised classification method for ontology matching and a recommendation mechanism that uses a sophisticated way from neighborhood-based collaborative and content-based filtering techniques to provide a personalized educational environment for each student.