A Comprehensive Review of Dropout Prediction Methods Based on Multivariate Analysed Features of MOOC Platforms
Massive open online courses have revolutionised the learning environment, but their effectiveness is undermined by low completion rates. Traditional dropout prediction models in MOOCs often overlook complex factors like temporal dependencies and context-specific variables. These models are not adapt...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Multimodal Technologies and Interaction |
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| Online Access: | https://www.mdpi.com/2414-4088/9/1/3 |
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| author | Saad Alghamdi Ben Soh Alice Li |
| author_facet | Saad Alghamdi Ben Soh Alice Li |
| author_sort | Saad Alghamdi |
| collection | DOAJ |
| description | Massive open online courses have revolutionised the learning environment, but their effectiveness is undermined by low completion rates. Traditional dropout prediction models in MOOCs often overlook complex factors like temporal dependencies and context-specific variables. These models are not adaptive enough to manage the dynamic nature of MOOC learning environments, resulting in inaccurate predictions and ineffective interventions. Accordingly, MOOCs dropout prediction models require more sophisticated artificial intelligence models that can address these limitations. Moreover, incorporating feature selection methods and explainable AI techniques can enhance the interpretability of these models, making them more actionable for educators and course designers. This paper provides a comprehensive review of various MOOCs dropout prediction methodologies, focusing on their strategies and research gaps. It highlights the growing MOOC environment and the potential for technology-driven gains in outcome accuracy. This review also discusses the use of advanced models based on machine learning, deep learning, and meta-heuristics approaches to improve course completion rates, optimise learning outcomes, and provide personalised educational experiences. |
| format | Article |
| id | doaj-art-59942fec320548c39bf0664865ee1869 |
| institution | Kabale University |
| issn | 2414-4088 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Multimodal Technologies and Interaction |
| spelling | doaj-art-59942fec320548c39bf0664865ee18692025-01-24T13:44:02ZengMDPI AGMultimodal Technologies and Interaction2414-40882025-01-0191310.3390/mti9010003A Comprehensive Review of Dropout Prediction Methods Based on Multivariate Analysed Features of MOOC PlatformsSaad Alghamdi0Ben Soh1Alice Li2Department of Computer Science and Information Technology, School of Computing, Engineering and Mathematical Sciences, La Trobe University, Bundoora, VIC 3086, AustraliaDepartment of Computer Science and Information Technology, School of Computing, Engineering and Mathematical Sciences, La Trobe University, Bundoora, VIC 3086, AustraliaLa Trobe Business School, La Trobe University, Bundoora, VIC 3086, AustraliaMassive open online courses have revolutionised the learning environment, but their effectiveness is undermined by low completion rates. Traditional dropout prediction models in MOOCs often overlook complex factors like temporal dependencies and context-specific variables. These models are not adaptive enough to manage the dynamic nature of MOOC learning environments, resulting in inaccurate predictions and ineffective interventions. Accordingly, MOOCs dropout prediction models require more sophisticated artificial intelligence models that can address these limitations. Moreover, incorporating feature selection methods and explainable AI techniques can enhance the interpretability of these models, making them more actionable for educators and course designers. This paper provides a comprehensive review of various MOOCs dropout prediction methodologies, focusing on their strategies and research gaps. It highlights the growing MOOC environment and the potential for technology-driven gains in outcome accuracy. This review also discusses the use of advanced models based on machine learning, deep learning, and meta-heuristics approaches to improve course completion rates, optimise learning outcomes, and provide personalised educational experiences.https://www.mdpi.com/2414-4088/9/1/3MOOCsdropout predictionmeta-heuristicsdeep learningaccuracy optimisationfeature selection |
| spellingShingle | Saad Alghamdi Ben Soh Alice Li A Comprehensive Review of Dropout Prediction Methods Based on Multivariate Analysed Features of MOOC Platforms Multimodal Technologies and Interaction MOOCs dropout prediction meta-heuristics deep learning accuracy optimisation feature selection |
| title | A Comprehensive Review of Dropout Prediction Methods Based on Multivariate Analysed Features of MOOC Platforms |
| title_full | A Comprehensive Review of Dropout Prediction Methods Based on Multivariate Analysed Features of MOOC Platforms |
| title_fullStr | A Comprehensive Review of Dropout Prediction Methods Based on Multivariate Analysed Features of MOOC Platforms |
| title_full_unstemmed | A Comprehensive Review of Dropout Prediction Methods Based on Multivariate Analysed Features of MOOC Platforms |
| title_short | A Comprehensive Review of Dropout Prediction Methods Based on Multivariate Analysed Features of MOOC Platforms |
| title_sort | comprehensive review of dropout prediction methods based on multivariate analysed features of mooc platforms |
| topic | MOOCs dropout prediction meta-heuristics deep learning accuracy optimisation feature selection |
| url | https://www.mdpi.com/2414-4088/9/1/3 |
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