{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T08:01:15Z","timestamp":1772611275226,"version":"3.50.1"},"reference-count":47,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2022,2,4]],"date-time":"2022-02-04T00:00:00Z","timestamp":1643932800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,5,19]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Educational Data Mining (EDM) has become a promising research field for improving the quality of students and the education system. Although EDM dates back to several years, there is still lack of works for measuring and enhancing the computer programming skills of tertiary students. As such, we, in this paper, propose an EDM system for evaluating and improving tertiary students\u2019 programming skills. The proposed EDM system comprises two key modules for (i) classification process and (ii) learning process,. The classification module predicts the current status of a student and the learning process module helps generate respective suggestions and feedback to enhance the student\u2019s quality. In particular, for the classification module, we prepare a real dataset related to this task and evaluate the dataset to investigate six key Machine Learning (ML) algorithms, Support Vector Machine (SVM), decision tree, artificial neural network, Random Forest (RF), k-nearest neighbor and naive Bayes classifier, using accuracy-related performance measure metrics and goodness of the fit. The experimental results manifest that RF and SVM can predict the students more accurately than the other models. In addition, critical factors analysis is accomplished to identify the critical features toward achieving high classification accuracy. At last, we design an improvement mechanism in the learning process module that helps the students enhance their programming skills.<\/jats:p>","DOI":"10.1093\/comjnl\/bxab214","type":"journal-article","created":{"date-parts":[[2021,12,24]],"date-time":"2021-12-24T04:28:03Z","timestamp":1640320083000},"page":"1083-1101","source":"Crossref","is-referenced-by-count":15,"title":["An Educational Data Mining System For Predicting And Enhancing Tertiary Students\u2019 Programming Skill"],"prefix":"10.1093","volume":"66","author":[{"given":"Md Abu","family":"Marjan","sequence":"first","affiliation":[{"name":"Hajee Mohammad Danesh Science and Technology University , Dinajpur , Bangladesh"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Md Palash","family":"Uddin","sequence":"additional","affiliation":[{"name":"Hajee Mohammad Danesh Science and Technology University , Dinajpur , Bangladesh"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masud","family":"Ibn Afjal","sequence":"additional","affiliation":[{"name":"Hajee Mohammad Danesh Science and Technology University , Dinajpur , Bangladesh"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,2,4]]},"reference":[{"key":"2023052000434874100_ref1","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1093\/comjnl\/bxr077","article-title":"Orange4ws environment for service-oriented data mining","volume":"55","author":"Podpecan","year":"2012","journal-title":"The Computer Journal"},{"key":"2023052000434874100_ref2","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1093\/comjnl\/bxz051","article-title":"A novel data mining on breast cancer survivability using mlp ensemble learners","volume":"63","author":"Salehi","year":"2019","journal-title":"The Computer Journal"},{"key":"2023052000434874100_ref3","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.eswa.2006.04.005","article-title":"Educational data mining: A survey from 1995 to 2005","volume":"33","author":"Romero","year":"2007","journal-title":"Expert Systems with Applications"},{"key":"2023052000434874100_ref4","doi-asserted-by":"crossref","first-page":"121","DOI":"10.14686\/buefad.606077","article-title":"Educational data mining and learning analytics: Past, present and future","volume":"9","author":"\u015eah\u00edn","year":"2020","journal-title":"Bartin University Journal of Faculty of Education"},{"key":"2023052000434874100_ref5","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.tele.2019.01.007","article-title":"Educational data mining and learning analytics for 21st century higher education: A review and synthesis","volume":"37","author":"Aldowah","year":"2019","journal-title":"Telematics and Informatics"},{"key":"2023052000434874100_ref6","doi-asserted-by":"crossref","first-page":"1683","DOI":"10.1007\/s00521-018-3756-y","article-title":"Improving the evaluation process of students\u2019 performance utilizing a decision support software","volume":"31","author":"Livieris","year":"2019","journal-title":"Neural Comput. 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