{"title":"Modeling Learner Profiles using Ontologies and Machine Learning","authors":"Samia Bousalem, Fouzia Benchikha, Massinissa Chelghoum","doi":"10.1109/NTIC55069.2022.10100497","DOIUrl":null,"url":null,"abstract":"In recent years, E-learning technologies have altered the way we teach and learn, making it an intriguing research topic for enhancing education. A key component of these systems is the ability to tailor the learning experience to the needs of the individual student. According to researches, modeling student profiles with an ontology is quite relevant. However, the ontology must consider every aspect of learner representation. Therefore, there is an urgent need for new comprehensive information to improve the learner profile. In this paper, we propose a semantic approach to define an ontology of learner profiles. In addition, a learning style prediction system based on machine learning techniques is developed. Empirical results show a promising gain in performance for learning style prediction systems.","PeriodicalId":403927,"journal":{"name":"2022 2nd International Conference on New Technologies of Information and Communication (NTIC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 2nd International Conference on New Technologies of Information and Communication (NTIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NTIC55069.2022.10100497","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In recent years, E-learning technologies have altered the way we teach and learn, making it an intriguing research topic for enhancing education. A key component of these systems is the ability to tailor the learning experience to the needs of the individual student. According to researches, modeling student profiles with an ontology is quite relevant. However, the ontology must consider every aspect of learner representation. Therefore, there is an urgent need for new comprehensive information to improve the learner profile. In this paper, we propose a semantic approach to define an ontology of learner profiles. In addition, a learning style prediction system based on machine learning techniques is developed. Empirical results show a promising gain in performance for learning style prediction systems.