Knowledge-based recommendation system using semantic web rules based on Learning styles for MOOCs
With web-based education and Technology Enhanced Learning (TEL) assuming new importance, there has been a shift towards Massive Open Online Courses (MOOC) platforms owing to their openness and flexible “on-the-go” nature. The previous decade has seen tremendous research in the field of Adaptive E-Le...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | Cogent Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/23311916.2021.2022568 |
| _version_ | 1797766826650763264 |
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| author | Abhinav Agarwal Divyansh Shankar Mishra Sucheta V. Kolekar |
| author_facet | Abhinav Agarwal Divyansh Shankar Mishra Sucheta V. Kolekar |
| author_sort | Abhinav Agarwal |
| collection | DOAJ |
| description | With web-based education and Technology Enhanced Learning (TEL) assuming new importance, there has been a shift towards Massive Open Online Courses (MOOC) platforms owing to their openness and flexible “on-the-go” nature. The previous decade has seen tremendous research in the field of Adaptive E-Learning Systems but work in the field of personalization in MOOCs is still a promising avenue. This paper aims to discuss the scope of said personalization in a MOOC environment along with proposing an approach to build a knowledge-based recommendation system that uses multiple domain ontologies and operates on semantically related usage data. The recommendation system employs cluster-based collaborative filtering in conjunction with rules written in the Semantic Web Rule Language (SWRL) and thus is truly a hybrid recommendation system. It has at its core, clusters of learners which are segregated using predicted learning style in accordance with the Felder Silverman Learning Style Model (FSLSM) through the detection of tracked usage parameters. Recommendations are made to the granularity of internal course elements along with learning path recommendation and provided general learning tips and suggestions. The study is concluded with an observed positive trend in the learning experience of participants, gauged through click-through log and explicit feedback forms. In addition, the impact of recommendation is statistically analyzed and used to improve the recommendations. |
| first_indexed | 2024-03-12T20:30:50Z |
| format | Article |
| id | doaj.art-f7576e8799a0485cb3fa0549337b0dae |
| institution | Directory Open Access Journal |
| issn | 2331-1916 |
| language | English |
| last_indexed | 2024-03-12T20:30:50Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Engineering |
| spelling | doaj.art-f7576e8799a0485cb3fa0549337b0dae2023-08-02T00:08:57ZengTaylor & Francis GroupCogent Engineering2331-19162022-12-019110.1080/23311916.2021.20225682022568Knowledge-based recommendation system using semantic web rules based on Learning styles for MOOCsAbhinav Agarwal0Divyansh Shankar Mishra1Sucheta V. Kolekar2Manipal Institute of Technology, Manipal Academy of Higher Education (Mahe)Manipal Institute of Technology, Manipal Academy of Higher Education (Mahe)Manipal Institute of Technology, Manipal Academy of Higher Education (Mahe)With web-based education and Technology Enhanced Learning (TEL) assuming new importance, there has been a shift towards Massive Open Online Courses (MOOC) platforms owing to their openness and flexible “on-the-go” nature. The previous decade has seen tremendous research in the field of Adaptive E-Learning Systems but work in the field of personalization in MOOCs is still a promising avenue. This paper aims to discuss the scope of said personalization in a MOOC environment along with proposing an approach to build a knowledge-based recommendation system that uses multiple domain ontologies and operates on semantically related usage data. The recommendation system employs cluster-based collaborative filtering in conjunction with rules written in the Semantic Web Rule Language (SWRL) and thus is truly a hybrid recommendation system. It has at its core, clusters of learners which are segregated using predicted learning style in accordance with the Felder Silverman Learning Style Model (FSLSM) through the detection of tracked usage parameters. Recommendations are made to the granularity of internal course elements along with learning path recommendation and provided general learning tips and suggestions. The study is concluded with an observed positive trend in the learning experience of participants, gauged through click-through log and explicit feedback forms. In addition, the impact of recommendation is statistically analyzed and used to improve the recommendations.http://dx.doi.org/10.1080/23311916.2021.2022568collaborative filteringclusteringcontent-basedfelder silverman learning style modellearning styleontologyrecommendation systemrule-based filteringsemantic web |
| spellingShingle | Abhinav Agarwal Divyansh Shankar Mishra Sucheta V. Kolekar Knowledge-based recommendation system using semantic web rules based on Learning styles for MOOCs Cogent Engineering collaborative filtering clustering content-based felder silverman learning style model learning style ontology recommendation system rule-based filtering semantic web |
| title | Knowledge-based recommendation system using semantic web rules based on Learning styles for MOOCs |
| title_full | Knowledge-based recommendation system using semantic web rules based on Learning styles for MOOCs |
| title_fullStr | Knowledge-based recommendation system using semantic web rules based on Learning styles for MOOCs |
| title_full_unstemmed | Knowledge-based recommendation system using semantic web rules based on Learning styles for MOOCs |
| title_short | Knowledge-based recommendation system using semantic web rules based on Learning styles for MOOCs |
| title_sort | knowledge based recommendation system using semantic web rules based on learning styles for moocs |
| topic | collaborative filtering clustering content-based felder silverman learning style model learning style ontology recommendation system rule-based filtering semantic web |
| url | http://dx.doi.org/10.1080/23311916.2021.2022568 |
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