Enhancing e-learning through AI: advanced techniques for optimizing student performance
The integration of Artificial Intelligence (AI) into e-learning has transformed conventional educational approaches, improving the learning process and maximizing student achievement. This study offers a thorough examination of how AI can be utilized to enhance e-learning results by employing advanc...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2024-12-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2576.pdf |
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| author | Rund Mahafdah Seifeddine Bouallegue Ridha Bouallegue |
| author_facet | Rund Mahafdah Seifeddine Bouallegue Ridha Bouallegue |
| author_sort | Rund Mahafdah |
| collection | DOAJ |
| description | The integration of Artificial Intelligence (AI) into e-learning has transformed conventional educational approaches, improving the learning process and maximizing student achievement. This study offers a thorough examination of how AI can be utilized to enhance e-learning results by employing advanced predictive methods and performance optimization strategies. The main goals consist of creating an AI-based framework to monitor and analyze student interactions, evaluating the influence of online learning platforms on student understanding using advanced algorithms, and determining the most efficient methods for blended learning systems. AI algorithms, known for their cognitive ability and capacity to learn, adapt, and make decisions, are employed to analyze and forecast student performance, thereby improving educational quality and outcomes. The practical results obtained by implementing machine learning and deep learning models, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), show substantial enhancements in forecasting different performance metrics. This research highlights the ability of AI to develop adaptable, effective, and successful e-learning environments, promoting enhanced academic achievement and customized learning experiences. The findings demonstrate that CNN outperformed other deep learning and machine learning algorithms in terms of accuracy during the prediction phase, showcasing the advanced capabilities of AI in educational contexts. Portions of this text were previously published as part of a preprint (https://doi.org/10.21203/rs.3.rs-4724603/v1). |
| format | Article |
| id | doaj-art-607fe97ca76d4d48a2df35e9c360ce0a |
| institution | OA Journals |
| issn | 2376-5992 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-607fe97ca76d4d48a2df35e9c360ce0a2025-08-20T02:35:18ZengPeerJ Inc.PeerJ Computer Science2376-59922024-12-0110e257610.7717/peerj-cs.2576Enhancing e-learning through AI: advanced techniques for optimizing student performanceRund Mahafdah0Seifeddine Bouallegue1Ridha Bouallegue2Innov’COM Laboratory High School of Communications (Sup’COM), University of Carthage, Carthage, TunisiaUniversity of Doha for Science and Technology, Doha, QatarInnov’COM Laboratory High School of Communications (Sup’COM), University of Carthage, Carthage, TunisiaThe integration of Artificial Intelligence (AI) into e-learning has transformed conventional educational approaches, improving the learning process and maximizing student achievement. This study offers a thorough examination of how AI can be utilized to enhance e-learning results by employing advanced predictive methods and performance optimization strategies. The main goals consist of creating an AI-based framework to monitor and analyze student interactions, evaluating the influence of online learning platforms on student understanding using advanced algorithms, and determining the most efficient methods for blended learning systems. AI algorithms, known for their cognitive ability and capacity to learn, adapt, and make decisions, are employed to analyze and forecast student performance, thereby improving educational quality and outcomes. The practical results obtained by implementing machine learning and deep learning models, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), show substantial enhancements in forecasting different performance metrics. This research highlights the ability of AI to develop adaptable, effective, and successful e-learning environments, promoting enhanced academic achievement and customized learning experiences. The findings demonstrate that CNN outperformed other deep learning and machine learning algorithms in terms of accuracy during the prediction phase, showcasing the advanced capabilities of AI in educational contexts. Portions of this text were previously published as part of a preprint (https://doi.org/10.21203/rs.3.rs-4724603/v1).https://peerj.com/articles/cs-2576.pdfMachine learningDeep learningEducation dataAIArtificial Intelligence (AI)eLearning |
| spellingShingle | Rund Mahafdah Seifeddine Bouallegue Ridha Bouallegue Enhancing e-learning through AI: advanced techniques for optimizing student performance PeerJ Computer Science Machine learning Deep learning Education data AI Artificial Intelligence (AI) eLearning |
| title | Enhancing e-learning through AI: advanced techniques for optimizing student performance |
| title_full | Enhancing e-learning through AI: advanced techniques for optimizing student performance |
| title_fullStr | Enhancing e-learning through AI: advanced techniques for optimizing student performance |
| title_full_unstemmed | Enhancing e-learning through AI: advanced techniques for optimizing student performance |
| title_short | Enhancing e-learning through AI: advanced techniques for optimizing student performance |
| title_sort | enhancing e learning through ai advanced techniques for optimizing student performance |
| topic | Machine learning Deep learning Education data AI Artificial Intelligence (AI) eLearning |
| url | https://peerj.com/articles/cs-2576.pdf |
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