{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T18:46:38Z","timestamp":1772822798052,"version":"3.50.1"},"reference-count":20,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,4,9]],"date-time":"2021-04-09T00:00:00Z","timestamp":1617926400000},"content-version":"vor","delay-in-days":98,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009084","name":"Ningxia University","doi-asserted-by":"publisher","award":["YLXKZD1928"],"award-info":[{"award-number":["YLXKZD1928"]}],"id":[{"id":"10.13039\/100009084","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>With the continuous development of \u201cInternet\u2009+\u2009Education\u201d, online learning has become a hot topic of concern. Decision tree is an important technique for solving classification problems from a set of random and unordered data sets. Decision tree is not only an effective method to generate classifier from data set, but also an active research field in data mining technology. The decision tree mining algorithm can classify the data, grasp the teaching process of the teacher, and analyze the overall performance of the students, so as to realize the dynamic management of the educational administration and help the educational administration personnel to make the right decision, with more reasonable allocation of resources. This paper evaluates students\u2019 academic performance based on the learning behavior data of online learning, so as to intervene in students\u2019 learning in advance, which is the key problem that needs to be solved at present. Taking students\u2019 learning attitude, completion of homework, and attendance as factors, the paper uses decision tree technology to analyze the factors affecting students\u2019 performance, and evaluates students\u2019 performance. Firstly, this paper collects the high\u2010dimensional behavioral characteristic data of students\u2019 online learning and conducts correlation analysis after preprocessing the behavioral characteristic data. Then, the decision tree C4.5 algorithm is used to construct a performance evaluation model. Students\u2019 performance is evaluated by the model, and the evaluation accuracy is about 88% compared with actual performance. Finally, through the model analysis, it is concluded that the video task point completion is the most influential in students\u2019 achievement, followed by chapter test completion and chapter test average score, and the course interaction amount and homework average score are the least influential in students\u2019 achievement, which has a practical reference value for effectively serving online learning and teachers\u2019 teaching.<\/jats:p>","DOI":"10.1155\/2021\/5519647","type":"journal-article","created":{"date-parts":[[2021,4,9]],"date-time":"2021-04-09T20:35:11Z","timestamp":1618000511000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["The Evaluation of Online Education Course Performance Using Decision Tree Mining Algorithm"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0996-977X","authenticated-orcid":false,"given":"Yongxian","family":"Yang","sequence":"first","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,4,9]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.2991\/ijcis.d.191016.001"},{"key":"e_1_2_9_2_2","first-page":"1735","article-title":"Students performance prediction using decision tree","volume":"13","author":"Raut A. 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