Effective Graph Mining for Educational Data Mining and Interest Recommendation
In order to fully understand and analyze the rules and cognitive characteristics of users’ learning methods and, with the assistance of Internet and artificial acquaintance technology, to emphasize the integrity and degree of personalized education, a personalized graph-learning-based recommendation...
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Applied Bionics and Biomechanics |
| Online Access: | http://dx.doi.org/10.1155/2022/7610124 |
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| _version_ | 1850164567733174272 |
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| author | Shasha Xu |
| author_facet | Shasha Xu |
| author_sort | Shasha Xu |
| collection | DOAJ |
| description | In order to fully understand and analyze the rules and cognitive characteristics of users’ learning methods and, with the assistance of Internet and artificial acquaintance technology, to emphasize the integrity and degree of personalized education, a personalized graph-learning-based recommendation system including user portraits is proposed. System raking of data layers, data analysis responses, and recommendations for sum beds are seamless and collaboratively combined. The data layer consists of user data and a design library containing scholarship materials, study materials, and price sets. The data analysis framework is captured by rest and energy data represented by basic information, learning behavior, etc. We can provide perceptual and visual learning audio feedback. And thus witness computing should convey users’ learning behavior rules through similarity analysis and mob algorithm. We further use TF-IDF to sequentially mine users’ resource priorities and always bind personalized learning suggestions. The system has been applied to an online education platform supported by artificial intelligence technique, which can provide instructors and students with personalized portraits. We also proposed to learn audio feedback and data consulting services, typically during the hard work phase of the assistant semester. |
| format | Article |
| id | doaj-art-e9bd4a179d73435fa158e83f968812d3 |
| institution | OA Journals |
| issn | 1754-2103 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Bionics and Biomechanics |
| spelling | doaj-art-e9bd4a179d73435fa158e83f968812d32025-08-20T02:21:57ZengWileyApplied Bionics and Biomechanics1754-21032022-01-01202210.1155/2022/7610124Effective Graph Mining for Educational Data Mining and Interest RecommendationShasha Xu0Zhengzhou Preschool Education CollegeIn order to fully understand and analyze the rules and cognitive characteristics of users’ learning methods and, with the assistance of Internet and artificial acquaintance technology, to emphasize the integrity and degree of personalized education, a personalized graph-learning-based recommendation system including user portraits is proposed. System raking of data layers, data analysis responses, and recommendations for sum beds are seamless and collaboratively combined. The data layer consists of user data and a design library containing scholarship materials, study materials, and price sets. The data analysis framework is captured by rest and energy data represented by basic information, learning behavior, etc. We can provide perceptual and visual learning audio feedback. And thus witness computing should convey users’ learning behavior rules through similarity analysis and mob algorithm. We further use TF-IDF to sequentially mine users’ resource priorities and always bind personalized learning suggestions. The system has been applied to an online education platform supported by artificial intelligence technique, which can provide instructors and students with personalized portraits. We also proposed to learn audio feedback and data consulting services, typically during the hard work phase of the assistant semester.http://dx.doi.org/10.1155/2022/7610124 |
| spellingShingle | Shasha Xu Effective Graph Mining for Educational Data Mining and Interest Recommendation Applied Bionics and Biomechanics |
| title | Effective Graph Mining for Educational Data Mining and Interest Recommendation |
| title_full | Effective Graph Mining for Educational Data Mining and Interest Recommendation |
| title_fullStr | Effective Graph Mining for Educational Data Mining and Interest Recommendation |
| title_full_unstemmed | Effective Graph Mining for Educational Data Mining and Interest Recommendation |
| title_short | Effective Graph Mining for Educational Data Mining and Interest Recommendation |
| title_sort | effective graph mining for educational data mining and interest recommendation |
| url | http://dx.doi.org/10.1155/2022/7610124 |
| work_keys_str_mv | AT shashaxu effectivegraphminingforeducationaldataminingandinterestrecommendation |