{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T09:28:05Z","timestamp":1780392485567,"version":"3.54.1"},"reference-count":41,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2020,5,3]],"date-time":"2020-05-03T00:00:00Z","timestamp":1588464000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Student performance prediction has become a hot research topic. Most of the existing prediction models are built by a machine learning method. They are interested in prediction accuracy but pay less attention to interpretability. We propose a stacking ensemble model to predict and analyze student performance in academic competition. In this model, student performance is classified into two symmetrical categorical classes. To improve accuracy, three machine learning algorithms, including support vector machine (SVM), random forest, and AdaBoost are established in the first level and then integrated by logistic regression via stacking. A feature importance analysis was applied to identify important variables. The experimental data were collected from four academic years in Hankou University. According to comparative studies on five evaluation metrics (precision, recall, F1, error, and      area \u00a0 under \u00a0 the \u00a0 receiver \u00a0 operating \u00a0 characteristic \u00a0 curve      (    AUC    ) in this analysis, the proposed model generally performs better than compared models. The important variables identified from the analysis are interpretable, they can be used as guidance to select potential students.<\/jats:p>","DOI":"10.3390\/sym12050728","type":"journal-article","created":{"date-parts":[[2020,5,5]],"date-time":"2020-05-05T06:41:20Z","timestamp":1588660880000},"page":"728","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["An Ensemble Prediction Model for Potential Student Recommendation Using Machine Learning"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2889-4662","authenticated-orcid":false,"given":"Lijuan","family":"Yan","sequence":"first","affiliation":[{"name":"National Engineering Research Center for E-Learning, Central China Normal University, Wuhan 430079, China"},{"name":"Hubei Research Center for Educational Informationization, Central China Normal University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanshen","family":"Liu","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for E-Learning, Central China Normal University, Wuhan 430079, China"},{"name":"Hubei Research Center for Educational Informationization, Central China Normal University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1002\/ase.1498","article-title":"Head to head: The role of competition in undergraduate education","volume":"8","author":"Roach","year":"2015","journal-title":"Anat. 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