{"id":8674,"date":"2026-06-27T14:35:20","date_gmt":"2026-06-27T06:35:20","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=8674"},"modified":"2026-06-27T16:33:06","modified_gmt":"2026-06-27T08:33:06","slug":"jase-202610-33-022","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202610-33-022","title":{"rendered":"An Apriori-BN Hybrid Model for Mining Association Rules in Teaching Data"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 33<\/a><\/p>\n<\/div>\n\n\n\n

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Wei Zhu<\/i><\/a><\/p>\n\n\n\n

Yellow River Conservancy Technical University, Kaifeng, Henan 475004, China<\/p>\n<\/div>\n\n\n\n

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Received: April 1, 2026
Accepted: April 17, 2026
Publication Date: June 27, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Effect of Data Sparsity on Average Lift <\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202610_33.022<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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This paper proposes an Apriori-Bayesian Network hybrid model for mining association rules in teaching evaluation data. A total of 5,200 teaching evaluation records were cleaned, discretized, and transformed into transaction data. Apriori was first used to generate frequent itemsets under sensitivity-tested support and onfidence thresholds, and Bayesian network inference was then introduced to reduce redundant rules and improve rule interpretability. Experimental results show that the proposed model achieves higher rule quality than Apriori, FP-Growth, Eclat, and genetic association rule mining. The average rule redundancy is reduced by 18.7%, and the key rules obtain lift values greater than 1.30, indicating strong positive associations. The model also supports rule-based warning, performance prediction, and teaching quality diagnosis in a B\/S management system. The results demonstrate that the proposed method is effective for teaching data mining and decision support.<\/p>\n\n\n\n

Keywords: Data mining; Association rules; Teaching quality; Bayesian network<\/em><\/p>\n\n\n\n

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