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. Preprints2020, 2020080388. https://doi.org/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. https://doi.org/10.20944/preprints202008.0388.v1
Demertzi, V.; Demertzis, K. A Hybrid Adaptive Educational eLearning Project based on Ontologies Matching and Recommendation System. Preprints2020, 2020080388. https://doi.org/10.20944/preprints202008.0388.v1
APA Style
Demertzi, V., & Demertzis, K. (2020). A Hybrid Adaptive Educational eLearning Project based on Ontologies Matching and Recommendation System. Preprints. https://doi.org/10.20944/preprints202008.0388.v1
Chicago/Turabian Style
Demertzi, V. and Konstantinos Demertzis. 2020 "A Hybrid Adaptive Educational eLearning Project based on Ontologies Matching and Recommendation System" Preprints. https://doi.org/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.
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
