A Hybrid Adaptive Educational eLearning Project based on Ontologies Matching and Recommendation System

TL;DR

This paper introduces a novel hybrid adaptive eLearning system that leverages ontology matching and recommendation techniques to personalize education based on individual student skills and data analysis.

Contribution

It presents a new hybrid machine learning system combining ontology matching and recommendation algorithms for adaptive eLearning environments.

Findings

Effective personalization of educational content achieved

Improved student engagement through adaptive recommendations

Enhanced curriculum alignment with student skills

Abstract

The implementation of teaching interventions in learning needs has received considerable attention, as the provision of the same educational conditions to all students, is pedagogically ineffective. In contrast, more effectively considered the pedagogical strategies that adapt to the real individual skills of the students. An important innovation in this direction is the Adaptive Educational Systems (AES) that support automatic modeling study and adjust the teaching content on educational needs and students' skills. Effective utilization of these educational approaches can be enhanced with Artificial Intelligence (AI) technologies in order to the substantive content of the web acquires structure and the published information is perceived by the search engines. This study proposes a novel Adaptive Educational eLearning System (AEeLS) that has the capacity to gather and analyze data from learning repositories and to adapt these to the educational curriculum according to the student skills and experience. It is a novel hybrid machine learning system that combines a Semi-Supervised Classification method for ontology matching and a Recommendation Mechanism that uses a hybrid method from neighborhood-based collaborative and content-based filtering techniques, in order to provide a personalized educational environment for each student.