Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 17 August 2020 / Approved: 18 August 2020 / Online: 18 August 2020 (11:21:54 CEST)
Version 2 : Received: 22 August 2020 / Approved: 24 August 2020 / Online: 24 August 2020 (09:46:19 CEST)

How to cite: Demertzi, V.; Demertzis, K. A Hybrid Adaptive Educational eLearning Project based on Ontologies Matching and Recommendation System. Preprints 2020, 2020080388 (doi: 10.20944/preprints202008.0388.v1). Demertzi, V.; Demertzis, K. A Hybrid Adaptive Educational eLearning Project based on Ontologies Matching and Recommendation System. Preprints 2020, 2020080388 (doi: 10.20944/preprints202008.0388.v1).

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.

Subject Areas

Adaptive Educational System; E-Learning; Machine Learning; Semantics; Recommendation System; Ontologies Matching.

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.