{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T16:39:58Z","timestamp":1775839198126,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,9,26]],"date-time":"2021-09-26T00:00:00Z","timestamp":1632614400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Research Program of Chongqing Municipal Education Commission of China","award":["KJQN202003601,KJZD-K201903601"],"award-info":[{"award-number":["KJQN202003601,KJZD-K201903601"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>The university curriculum is a systematic and organic study complex with some immediate associated steps; the initial learning of each semester\u2019s course is crucial, and significantly impacts the learning process of subsequent courses and further studies. However, the low teacher\u2013student ratio makes it difficult for teachers to consistently follow up on the detail-oriented learning situation of individual students. The extant learning early warning system is committed to automatically detecting whether students have potential difficulties\u2014or even the risk of failing, or non-pass reports\u2014before starting the course. Previous related research has the following three problems: first of all, it mainly focused on e-learning platforms and relied on online activity data, which was not suitable for traditional teaching scenarios; secondly, most current methods can only proffer predictions when the course is in progress, or even approaching the end; thirdly, few studies have focused on the feature redundancy in these learning data. Aiming at the traditional classroom teaching scenario, this paper transforms the pre-class student performance prediction problem into a multi-label learning model, and uses the attribute reduction method to scientifically streamline the characteristic information of the courses taken and explore the important relationship between the characteristics of the previously learned courses and the attributes of the courses to be taken, in order to detect high-risk students in each course before the course begins. Extensive experiments were conducted on 10 real-world datasets, and the results proved that the proposed approach achieves better performance than most other advanced methods in multi-label classification evaluation metrics.<\/jats:p>","DOI":"10.3390\/e23101252","type":"journal-article","created":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T01:59:47Z","timestamp":1632707987000},"page":"1252","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Discriminable Multi-Label Attribute Selection for Pre-Course Student Performance Prediction"],"prefix":"10.3390","volume":"23","author":[{"given":"Jie","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Computer and Information Science, University of Macau, Taipa 999078, China"},{"name":"College of Artificial Intelligence, Chongqing Industry & Trade Polytechnic, Chongqing 408000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shimin","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science, University of Macau, Taipa 999078, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qichao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of International Relations, Xi\u2019an International Studies University, Xi\u2019an 710128, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1848-7246","authenticated-orcid":false,"given":"Simon","family":"Fong","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science, University of Macau, Taipa 999078, China"},{"name":"ZIAT DACC Laboratory, Zhuhai Institutes of Advanced Technology of the Chinese Academy of Sciences, Zhuhai 519000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103676","DOI":"10.1016\/j.compedu.2019.103676","article-title":"An overview and comparison of supervised data mining techniques for student exam performance prediction","volume":"143","author":"Tomasevic","year":"2019","journal-title":"Comput. 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