{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:49:52Z","timestamp":1776811792268,"version":"3.51.2"},"reference-count":13,"publisher":"European Society of Computational Methods in Sciences and Engineering","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"published-print":{"date-parts":[[2023,12,15]]},"abstract":"<jats:p>Predicting students\u2019 course grades is an essential element in teaching. This paper used decision tree generation rules to study the prediction of students\u2019 ideological and political course grades. Firstly, ID3 and C4.5 algorithms were briefly introduced; then, an improved C4.5 algorithm with higher computational efficiency was put forward. The formula of the C4.5 algorithm was optimized using theories such as the Taylor series. Finally, experiments were performed on the UCI dataset and students\u2019 ideological and political course datasets. The results suggested that the average classification accuracy and computation time of the improved C4.5 algorithm was 79.37% and 74.1 ms, respectively, on the UCI dataset, which was better than the traditional C4.5 algorithm. Then, the experiment predicting students\u2019 course grades demonstrated that the average quiz grade and the number of video views had the greatest impact on the final grades. The prediction accuracy of the improved C4.5 algorithm reached 93.46%, and the average computation time was 54.8 ms, which was 19.17% less than the C4.5 algorithm. The experimental results verify the effectiveness of the generation rule of the improved C4.5 algorithm in predicting students\u2019 ideological and political course grades. This algorithm can be applied in the actual grade prediction.<\/jats:p>","DOI":"10.3233\/jcm-226953","type":"journal-article","created":{"date-parts":[[2023,12,19]],"date-time":"2023-12-19T12:20:51Z","timestamp":1702988451000},"page":"3219-3228","source":"Crossref","is-referenced-by-count":1,"title":["A study on predicting students\u2019 grades for ideological and political courses with decision tree generation rules"],"prefix":"10.66113","volume":"23","author":[{"given":"Jianwei","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Marxism, Yanching Institute of Technology, Sanhe, Hebei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjing","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Yanching Institute of Technology, Sanhe, Hebei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"55691","reference":[{"issue":"02","key":"10.3233\/JCM-226953_ref1","first-page":"200","article-title":"Pre-course student performance prediction with multi-instance multi-label learning","volume":"62","author":"Ma","year":"2019","journal-title":"Science China (Information Sciences)"},{"issue":"1","key":"10.3233\/JCM-226953_ref2","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1016\/j.compeleceng.2017.03.005","article-title":"Data mining for modeling students\u2019 performance: A tutoring action plan to prevent academic dropout","volume":"66","author":"Burgos","year":"2018","journal-title":"Computers and Electrical Engineering"},{"issue":"8","key":"10.3233\/JCM-226953_ref3","first-page":"3824","article-title":"Student future prediction system under filtering mechanism","volume":"17","author":"Vimali","year":"2020","journal-title":"Journal of Computational and Theoretical 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Computer Systems: FGCS"},{"key":"10.3233\/JCM-226953_ref9","first-page":"1","article-title":"Next-term student performance prediction: A recommender systems approach","volume":"8","author":"Sweeney","year":"2016","journal-title":"Journal of Educational Data Mining"},{"issue":"6","key":"10.3233\/JCM-226953_ref10","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1007\/s11633-021-1312-1","article-title":"Data Augmentation and Deep Neuro-fuzzy Network for Student Performance Prediction with MapReduce Framework","volume":"18","author":"Baruah","year":"2021","journal-title":"International Journal of Automation and Computing"},{"issue":"3","key":"10.3233\/JCM-226953_ref12","doi-asserted-by":"crossref","first-page":"16","DOI":"10.30564\/jcsr.v3i3.3534","article-title":"Student Performance Prediction Using A Cascaded Bi-level Feature Selection Approach","volume":"3","author":"Abdullahi","year":"2021","journal-title":"Journal of Computer Science Research"},{"key":"10.3233\/JCM-226953_ref13","first-page":"1166","article-title":"Rapid identification model based on decision tree algorithm coupling with H-1 NMR and feature analysis by UHPLC-QTOFMS spectrometry for sandalwood","volume":"1161","author":"Tan","year":"2021","journal-title":"Journal of Chromatography B \u2013 Analytical Technologies in the Biomedical and Life Sciences"},{"issue":"1","key":"10.3233\/JCM-226953_ref14","doi-asserted-by":"crossref","first-page":"12937","DOI":"10.1149\/10701.12937ecst","article-title":"Examination of diabetes mellitus for early forecast using decision tree classifier and an innovative dependent feature vector based naive bayes classifier","volume":"107","author":"Pradeepika","year":"2022","journal-title":"ECS Transactions"},{"key":"10.3233\/JCM-226953_ref15","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.1016\/j.egyr.2022.01.239","article-title":"Short term effect evaluation model of rural energy construction revitalization based on ID3 decision tree algorithm","volume":"8","author":"An","year":"2022","journal-title":"Energy Reports"},{"issue":"4","key":"10.3233\/JCM-226953_ref16","doi-asserted-by":"crossref","first-page":"293","DOI":"10.32604\/csse.2020.35.293","article-title":"Decision tree algorithm for precision marketing via network channel","volume":"35","author":"Zheng","year":"2020","journal-title":"International Journal of Computer Systems Science & Engineering"}],"container-title":["Journal of Computational Methods in Sciences and 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