Content Modeling in Smart Learning Environments: A systematic literature review
Educational content has become a key element for improving the quality and effectiveness of teaching. Many studies have been conducted on user and knowledge modeling using machine-learning algorithms in smart-learning environments. However, few studies have focused on content modeling to estimate co...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Graz University of Technology
2024-03-01
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| Series: | Journal of Universal Computer Science |
| Subjects: | |
| Online Access: | https://lib.jucs.org/article/106023/download/pdf/ |
| _version_ | 1797234292069236736 |
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| author | Alberto Jiménez-Macías Pedro J. Muñoz-Merino Margarita Ortiz-Rojas Mario Muñoz-Organero Carlos Delgado Kloos |
| author_facet | Alberto Jiménez-Macías Pedro J. Muñoz-Merino Margarita Ortiz-Rojas Mario Muñoz-Organero Carlos Delgado Kloos |
| author_sort | Alberto Jiménez-Macías |
| collection | DOAJ |
| description | Educational content has become a key element for improving the quality and effectiveness of teaching. Many studies have been conducted on user and knowledge modeling using machine-learning algorithms in smart-learning environments. However, few studies have focused on content modeling to estimate content indicators based on student interaction. This study presents a systematic literature review of content modeling using machine learning algorithms in smart learning environments. Two databases were used: Scopus and Web of Science (WoS), with studies conducted until August 2023. In addition, a manual search was performed at conferences and in relevant journals in the area. The results showed that assessment was the most used content in the papers, with difficulty and discrimination as the most common indicators. Item Response Theory (IRT) is the most commonly used technique; however, some studies have used different traditional learning algorithms such as Random Forest, Neural Networks, and Regression. Other indicators, such as time, grade, and number of attempts, were also estimated. Owing to the few studies on content modeling using machine learning algorithms based on interactions, this study presents new lines of research based on the results obtained in the literature review. |
| first_indexed | 2024-04-24T16:29:44Z |
| format | Article |
| id | doaj.art-cc8720a44ce8420f96e2f700ea0058bb |
| institution | Directory Open Access Journal |
| issn | 0948-6968 |
| language | English |
| last_indexed | 2024-04-24T16:29:44Z |
| publishDate | 2024-03-01 |
| publisher | Graz University of Technology |
| record_format | Article |
| series | Journal of Universal Computer Science |
| spelling | doaj.art-cc8720a44ce8420f96e2f700ea0058bb2024-03-30T07:33:12ZengGraz University of TechnologyJournal of Universal Computer Science0948-69682024-03-0130333336210.3897/jucs.106023106023Content Modeling in Smart Learning Environments: A systematic literature reviewAlberto Jiménez-Macías0Pedro J. Muñoz-Merino1Margarita Ortiz-Rojas2Mario Muñoz-Organero3Carlos Delgado Kloos4Universidad Carlos III de MadridUniversidad Carlos III de MadridEscuela Superior Politcnica del LitoralUniversidad Carlos III de MadridUniversidad Carlos III de MadridEducational content has become a key element for improving the quality and effectiveness of teaching. Many studies have been conducted on user and knowledge modeling using machine-learning algorithms in smart-learning environments. However, few studies have focused on content modeling to estimate content indicators based on student interaction. This study presents a systematic literature review of content modeling using machine learning algorithms in smart learning environments. Two databases were used: Scopus and Web of Science (WoS), with studies conducted until August 2023. In addition, a manual search was performed at conferences and in relevant journals in the area. The results showed that assessment was the most used content in the papers, with difficulty and discrimination as the most common indicators. Item Response Theory (IRT) is the most commonly used technique; however, some studies have used different traditional learning algorithms such as Random Forest, Neural Networks, and Regression. Other indicators, such as time, grade, and number of attempts, were also estimated. Owing to the few studies on content modeling using machine learning algorithms based on interactions, this study presents new lines of research based on the results obtained in the literature review.https://lib.jucs.org/article/106023/download/pdf/Content modelingSmart contentLearning analytic |
| spellingShingle | Alberto Jiménez-Macías Pedro J. Muñoz-Merino Margarita Ortiz-Rojas Mario Muñoz-Organero Carlos Delgado Kloos Content Modeling in Smart Learning Environments: A systematic literature review Journal of Universal Computer Science Content modeling Smart content Learning analytic |
| title | Content Modeling in Smart Learning Environments: A systematic literature review |
| title_full | Content Modeling in Smart Learning Environments: A systematic literature review |
| title_fullStr | Content Modeling in Smart Learning Environments: A systematic literature review |
| title_full_unstemmed | Content Modeling in Smart Learning Environments: A systematic literature review |
| title_short | Content Modeling in Smart Learning Environments: A systematic literature review |
| title_sort | content modeling in smart learning environments a systematic literature review |
| topic | Content modeling Smart content Learning analytic |
| url | https://lib.jucs.org/article/106023/download/pdf/ |
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