Dynamic educational recommender system based on Improved LSTM neural network
Abstract Nowadays, virtual learning environments have become widespread to avoid time and space constraints and share high-quality learning resources. As a result of human–computer interaction, student behaviors are recorded instantly. This work aims to design an educational recommendation system ac...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-02-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-54729-y |
| _version_ | 1797275206189842432 |
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| author | Hadis Ahmadian Yazdi Seyyed Javad Seyyed Mahdavi Hooman Ahmadian Yazdi |
| author_facet | Hadis Ahmadian Yazdi Seyyed Javad Seyyed Mahdavi Hooman Ahmadian Yazdi |
| author_sort | Hadis Ahmadian Yazdi |
| collection | DOAJ |
| description | Abstract Nowadays, virtual learning environments have become widespread to avoid time and space constraints and share high-quality learning resources. As a result of human–computer interaction, student behaviors are recorded instantly. This work aims to design an educational recommendation system according to the individual's interests in educational resources. This system is evaluated based on clicking or downloading the source with the help of the user so that the appropriate resources can be suggested to users. In online tutorials, in addition to the problem of choosing the right source, we face the challenge of being aware of diversity in users' preferences and tastes, especially their short-term interests in the near future, at the beginning of a session. We assume that the user's interests consist of two parts: (1) the user's long-term interests, which include the user's constant interests based on the history of the user's dynamic activities, and (2) the user's short-term interests, which indicate the user's current interests. Due to the use of Bilstm networks and their gradual learning feature, the proposed model supports learners' behavioral changes. An average accuracy of 0.9978 and a Loss of 0.0051 offer more appropriate recommendations than similar works. |
| first_indexed | 2024-03-07T15:11:00Z |
| format | Article |
| id | doaj.art-54aa1b1faa154b569fafec77eaf9a750 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-07T15:11:00Z |
| publishDate | 2024-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-54aa1b1faa154b569fafec77eaf9a7502024-03-05T18:39:25ZengNature PortfolioScientific Reports2045-23222024-02-0114111910.1038/s41598-024-54729-yDynamic educational recommender system based on Improved LSTM neural networkHadis Ahmadian Yazdi0Seyyed Javad Seyyed Mahdavi1Hooman Ahmadian Yazdi2Department of Computer Engineering, Neyshabur Branch, Islamic Azad UniversityDepartment of Electrical Engineering, Mashhad Branch, Islamic Azad UniversityDepartment of Computer Engineering, Ferdowsi University of MashhadAbstract Nowadays, virtual learning environments have become widespread to avoid time and space constraints and share high-quality learning resources. As a result of human–computer interaction, student behaviors are recorded instantly. This work aims to design an educational recommendation system according to the individual's interests in educational resources. This system is evaluated based on clicking or downloading the source with the help of the user so that the appropriate resources can be suggested to users. In online tutorials, in addition to the problem of choosing the right source, we face the challenge of being aware of diversity in users' preferences and tastes, especially their short-term interests in the near future, at the beginning of a session. We assume that the user's interests consist of two parts: (1) the user's long-term interests, which include the user's constant interests based on the history of the user's dynamic activities, and (2) the user's short-term interests, which indicate the user's current interests. Due to the use of Bilstm networks and their gradual learning feature, the proposed model supports learners' behavioral changes. An average accuracy of 0.9978 and a Loss of 0.0051 offer more appropriate recommendations than similar works.https://doi.org/10.1038/s41598-024-54729-yDeep learning networksRecurrent methodsEducational resource recommender system |
| spellingShingle | Hadis Ahmadian Yazdi Seyyed Javad Seyyed Mahdavi Hooman Ahmadian Yazdi Dynamic educational recommender system based on Improved LSTM neural network Scientific Reports Deep learning networks Recurrent methods Educational resource recommender system |
| title | Dynamic educational recommender system based on Improved LSTM neural network |
| title_full | Dynamic educational recommender system based on Improved LSTM neural network |
| title_fullStr | Dynamic educational recommender system based on Improved LSTM neural network |
| title_full_unstemmed | Dynamic educational recommender system based on Improved LSTM neural network |
| title_short | Dynamic educational recommender system based on Improved LSTM neural network |
| title_sort | dynamic educational recommender system based on improved lstm neural network |
| topic | Deep learning networks Recurrent methods Educational resource recommender system |
| url | https://doi.org/10.1038/s41598-024-54729-y |
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