Enhancing hyperparameters of LSTM network models through genetic algorithm for virtual learning environment prediction
In today's technology-driven era, innovative methods for predicting behaviors and patterns are crucial. Virtual Learning Environments (VLEs) represent a rich domain for exploration due to their abundant data and potential for enhancing learning experiences. Long Short-Term Memory (LSTM) models,...
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Polska Akademia Nauk
2025
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| Online Access: | http://eprints.utem.edu.my/id/eprint/29520/2/0269501012026155162874.pdf http://eprints.utem.edu.my/id/eprint/29520/ https://ijet.pl/index.php/ijet/article/view/10.24425-ijet.2025.155475 |
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| author | Ismanto, Edi Ab Ghani, Hadhrami Md Saleh, Nurul Izrin |
| author_facet | Ismanto, Edi Ab Ghani, Hadhrami Md Saleh, Nurul Izrin |
| author_sort | Ismanto, Edi |
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| content_provider | Universiti Teknikal Malaysia Melaka |
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| continent | Asia |
| country | Malaysia |
| description | In today's technology-driven era, innovative methods for predicting behaviors and patterns are crucial. Virtual Learning Environments (VLEs) represent a rich domain for exploration due to their abundant data and potential for enhancing learning experiences. Long Short-Term Memory (LSTM) models, while proficient with sequential data, face challenges such as overfitting and gradient issues. This study investigates the optimization of LSTM parameters and hyperparameters for VLE prediction. Adaptive gradient-based algorithms, including ADAM, NADAM, ADADELTA, ADAGRAD, and ADAMAX, exhibited superior performance. The LSTM model with ADADELTA achieved 91% accuracy for BBB course data, while ADAGRAD LSTM models attained average accuracies of 80% and 85% for DDD and FFF courses, respectively. Genetic algorithms for hyperparameter optimization significantly contributed, with the GA + LSTM + ADAGRAD model achieving 88% and 87% accuracy in the 7th and 9th models for BBB course data. The GA + LSTM + ADADELTA model produced average accuracy rates of 80% and 84% in DDD and FFF course data, with the highest accuracy rates of 86% and 93%, as well. These findings highlight the effectiveness of adaptive and genetic algorithms in enhancing LSTM model performance for VLE prediction, offering valuable insights for educational technology advancement. |
| format | Article |
| id | my.utem.eprints-29520 |
| institution | Universiti Teknikal Malaysia Melaka |
| language | en |
| publishDate | 2025 |
| publisher | Polska Akademia Nauk |
| record_format | eprints |
| spelling | my.utem.eprints-295202026-02-23T01:32:14Z http://eprints.utem.edu.my/id/eprint/29520/ Enhancing hyperparameters of LSTM network models through genetic algorithm for virtual learning environment prediction Ismanto, Edi Ab Ghani, Hadhrami Md Saleh, Nurul Izrin In today's technology-driven era, innovative methods for predicting behaviors and patterns are crucial. Virtual Learning Environments (VLEs) represent a rich domain for exploration due to their abundant data and potential for enhancing learning experiences. Long Short-Term Memory (LSTM) models, while proficient with sequential data, face challenges such as overfitting and gradient issues. This study investigates the optimization of LSTM parameters and hyperparameters for VLE prediction. Adaptive gradient-based algorithms, including ADAM, NADAM, ADADELTA, ADAGRAD, and ADAMAX, exhibited superior performance. The LSTM model with ADADELTA achieved 91% accuracy for BBB course data, while ADAGRAD LSTM models attained average accuracies of 80% and 85% for DDD and FFF courses, respectively. Genetic algorithms for hyperparameter optimization significantly contributed, with the GA + LSTM + ADAGRAD model achieving 88% and 87% accuracy in the 7th and 9th models for BBB course data. The GA + LSTM + ADADELTA model produced average accuracy rates of 80% and 84% in DDD and FFF course data, with the highest accuracy rates of 86% and 93%, as well. These findings highlight the effectiveness of adaptive and genetic algorithms in enhancing LSTM model performance for VLE prediction, offering valuable insights for educational technology advancement. Polska Akademia Nauk 2025 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/29520/2/0269501012026155162874.pdf Ismanto, Edi and Ab Ghani, Hadhrami and Md Saleh, Nurul Izrin (2025) Enhancing hyperparameters of LSTM network models through genetic algorithm for virtual learning environment prediction. International Journal of Electronics and Telecommunications, 71 (4). pp. 1-10. ISSN 2081-8491 https://ijet.pl/index.php/ijet/article/view/10.24425-ijet.2025.155475 10.24425/ijet.2025.155475 |
| spellingShingle | Ismanto, Edi Ab Ghani, Hadhrami Md Saleh, Nurul Izrin Enhancing hyperparameters of LSTM network models through genetic algorithm for virtual learning environment prediction |
| title | Enhancing hyperparameters of LSTM network models through genetic algorithm for virtual learning environment prediction |
| title_full | Enhancing hyperparameters of LSTM network models through genetic algorithm for virtual learning environment prediction |
| title_fullStr | Enhancing hyperparameters of LSTM network models through genetic algorithm for virtual learning environment prediction |
| title_full_unstemmed | Enhancing hyperparameters of LSTM network models through genetic algorithm for virtual learning environment prediction |
| title_short | Enhancing hyperparameters of LSTM network models through genetic algorithm for virtual learning environment prediction |
| title_sort | enhancing hyperparameters of lstm network models through genetic algorithm for virtual learning environment prediction |
| url | http://eprints.utem.edu.my/id/eprint/29520/2/0269501012026155162874.pdf http://eprints.utem.edu.my/id/eprint/29520/ https://ijet.pl/index.php/ijet/article/view/10.24425-ijet.2025.155475 |
| url_provider | http://eprints.utem.edu.my/ |