Intelligent Teaching Recommendation Model for Practical Discussion Course of Higher Education Based on Naive Bayes Machine Learning and Improved <i>k</i>-NN Data Mining Algorithm
Aiming at the existing problems in practical teaching in higher education, we construct an intelligent teaching recommendation model for a higher education practical discussion course based on naive Bayes machine learning and an improved <i>k</i>-NN data mining algorithm. Firstly, we est...
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MDPI AG
2025-06-01
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| author | Xiao Zhou Ling Guo Rui Li Ling Liu Juan Pan |
| author_facet | Xiao Zhou Ling Guo Rui Li Ling Liu Juan Pan |
| author_sort | Xiao Zhou |
| collection | DOAJ |
| description | Aiming at the existing problems in practical teaching in higher education, we construct an intelligent teaching recommendation model for a higher education practical discussion course based on naive Bayes machine learning and an improved <i>k</i>-NN data mining algorithm. Firstly, we establish the naive Bayes machine learning algorithm to achieve accurate classification of the students in the class and then implement student grouping based on this accurate classification. Then, relying on the student grouping, we use the matching features between the students’ interest vector and the practical topic vector to construct an intelligent teaching recommendation model based on an improved <i>k</i>-NN data mining algorithm, in which the optimal complete binary encoding tree for the discussion topic is modeled. Based on the encoding tree model, an improved <i>k</i>-NN algorithm recommendation model is established to match the student group interests and recommend discussion topics. The experimental results prove that our proposed recommendation algorithm (PRA) can accurately recommend discussion topics for different student groups, match the interests of each group to the greatest extent, and improve the students’ enthusiasm for participating in practical discussions. As for the control groups of the user-based collaborative filtering recommendation algorithm (UCFA) and the item-based collaborative filtering recommendation algorithm (ICFA), under the experimental conditions of the single dataset and multiple datasets, the PRA has higher accuracy, recall rate, precision, and F1 value than the UCFA and ICFA and has better recommendation performance and robustness. |
| format | Article |
| id | doaj-art-9ee0ddfd03ea495481075495631d16b2 |
| institution | Kabale University |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj-art-9ee0ddfd03ea495481075495631d16b22025-08-20T03:24:39ZengMDPI AGInformation2078-24892025-06-0116651210.3390/info16060512Intelligent Teaching Recommendation Model for Practical Discussion Course of Higher Education Based on Naive Bayes Machine Learning and Improved <i>k</i>-NN Data Mining AlgorithmXiao Zhou0Ling Guo1Rui Li2Ling Liu3Juan Pan4Postdoctoral Innovation Practice Base of Sichuan Province, Leshan Vocational and Technical College, Leshan 614000, ChinaDepartment of Military Logistic, Army Logistics Academy, Chongqing 401331, ChinaDepartment of Military Logistic, Army Logistics Academy, Chongqing 401331, ChinaChongqing Vocational Institute of Engineering, Chongqing 402260, ChinaPostdoctoral Innovation Practice Base of Sichuan Province, Leshan Vocational and Technical College, Leshan 614000, ChinaAiming at the existing problems in practical teaching in higher education, we construct an intelligent teaching recommendation model for a higher education practical discussion course based on naive Bayes machine learning and an improved <i>k</i>-NN data mining algorithm. Firstly, we establish the naive Bayes machine learning algorithm to achieve accurate classification of the students in the class and then implement student grouping based on this accurate classification. Then, relying on the student grouping, we use the matching features between the students’ interest vector and the practical topic vector to construct an intelligent teaching recommendation model based on an improved <i>k</i>-NN data mining algorithm, in which the optimal complete binary encoding tree for the discussion topic is modeled. Based on the encoding tree model, an improved <i>k</i>-NN algorithm recommendation model is established to match the student group interests and recommend discussion topics. The experimental results prove that our proposed recommendation algorithm (PRA) can accurately recommend discussion topics for different student groups, match the interests of each group to the greatest extent, and improve the students’ enthusiasm for participating in practical discussions. As for the control groups of the user-based collaborative filtering recommendation algorithm (UCFA) and the item-based collaborative filtering recommendation algorithm (ICFA), under the experimental conditions of the single dataset and multiple datasets, the PRA has higher accuracy, recall rate, precision, and F1 value than the UCFA and ICFA and has better recommendation performance and robustness.https://www.mdpi.com/2078-2489/16/6/512naive Bayesmachine learning<i>k</i>-NN data miningteaching recommendation modeldiscussion topic in class |
| spellingShingle | Xiao Zhou Ling Guo Rui Li Ling Liu Juan Pan Intelligent Teaching Recommendation Model for Practical Discussion Course of Higher Education Based on Naive Bayes Machine Learning and Improved <i>k</i>-NN Data Mining Algorithm Information naive Bayes machine learning <i>k</i>-NN data mining teaching recommendation model discussion topic in class |
| title | Intelligent Teaching Recommendation Model for Practical Discussion Course of Higher Education Based on Naive Bayes Machine Learning and Improved <i>k</i>-NN Data Mining Algorithm |
| title_full | Intelligent Teaching Recommendation Model for Practical Discussion Course of Higher Education Based on Naive Bayes Machine Learning and Improved <i>k</i>-NN Data Mining Algorithm |
| title_fullStr | Intelligent Teaching Recommendation Model for Practical Discussion Course of Higher Education Based on Naive Bayes Machine Learning and Improved <i>k</i>-NN Data Mining Algorithm |
| title_full_unstemmed | Intelligent Teaching Recommendation Model for Practical Discussion Course of Higher Education Based on Naive Bayes Machine Learning and Improved <i>k</i>-NN Data Mining Algorithm |
| title_short | Intelligent Teaching Recommendation Model for Practical Discussion Course of Higher Education Based on Naive Bayes Machine Learning and Improved <i>k</i>-NN Data Mining Algorithm |
| title_sort | intelligent teaching recommendation model for practical discussion course of higher education based on naive bayes machine learning and improved i k i nn data mining algorithm |
| topic | naive Bayes machine learning <i>k</i>-NN data mining teaching recommendation model discussion topic in class |
| url | https://www.mdpi.com/2078-2489/16/6/512 |
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