{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T12:00:21Z","timestamp":1777291221039,"version":"3.51.4"},"reference-count":38,"publisher":"Walter de Gruyter GmbH","issue":"2","license":[{"start":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T00:00:00Z","timestamp":1638316800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    The academic performance of students is essential for academic progression at all levels of education. However, the availability of several cognitive and non-cognitive factors that influence students\u2019 academic performance makes it challenging for academic authorities to use conventional analytical tools to extract hidden knowledge in educational data. Therefore, Educational Data Mining (EDM) requires computational techniques to simplify planning and determining students who might be at risk of failing or dropping from school due to academic performance, thus helping resolve student retention. The paper studies several cognitive and non-cognitive factors such as academic, demographic, social and behavioural and their effect on student academic performance using machine learning algorithms. Heterogenous lazy and eager machine learning classifiers, including Decision Tree (DT),\n                    <jats:italic>K<\/jats:italic>\n                    -Nearest-Neighbour (KNN), Artificial Neural Network (ANN), Logistic Regression (LR), Random Forest (RF), AdaBoost and Support Vector Machine (SVM) were adopted and training was performed based on k-fold (\n                    <jats:italic>k<\/jats:italic>\n                    = 10) and leave-one-out cross-validation. We evaluated their predictive performance using well-known evaluation metrics like Area under Curve (AUC), F-1 score, Precision, Accuracy, Kappa, Matthew\u2019s correlation coefficient (MCC) and Recall. The study outcome shows that Student Absence Days (SAD) are the most significant predictor of students\u2019 academic performance. In terms of prediction accuracy and AUC, the RF (Acc = 0.771, AUC = 0.903), LR (Acc = 0.779, AUC = 0.90) and ANN (Acc = 0.760, AUC = 0.895) outperformed all other algorithms (KNN (Acc = 0.638, AUC = 0.826), SVM (Acc = 0.727, AUC = 0.80), DT (Acc = 0.733, AUC = 0.876) and AdaBoost (Acc = 0.748, AUC = 0.808)), making them more suitable for predicting students\u2019 academic performance.\n                  <\/jats:p>","DOI":"10.2478\/acss-2021-0015","type":"journal-article","created":{"date-parts":[[2022,1,24]],"date-time":"2022-01-24T08:30:32Z","timestamp":1643013032000},"page":"122-131","source":"Crossref","is-referenced-by-count":15,"title":["Academic Performance Modelling with Machine Learning Based on Cognitive and Non-Cognitive Features"],"prefix":"10.2478","volume":"26","author":[{"given":"Bridgitte","family":"Owusu-Boadu","sequence":"first","affiliation":[{"name":"Brivink Consult & Technology , Sunyani , Ghana"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9257-4295","authenticated-orcid":false,"given":"Isaac Kofi","family":"Nti","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Informatics , University of Energy and Natural Resources , Sunyani , Ghana"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0300-2469","authenticated-orcid":false,"given":"Owusu","family":"Nyarko-Boateng","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Informatics , University of Energy and Natural Resources , Sunyani , Ghana"}]},{"given":"Justice","family":"Aning","sequence":"additional","affiliation":[{"name":"Department of Computer Science , Sunyani Technical University , Sunyani , Ghana"}]},{"given":"Victoria","family":"Boafo","sequence":"additional","affiliation":[{"name":"Department of Computer Science & ICT , Mampong Technical College of Education , Mampong , Ghana"}]}],"member":"374","published-online":{"date-parts":[[2021,12,30]]},"reference":[{"key":"2026042709092575281_j_acss-2021-0015_ref_001","doi-asserted-by":"crossref","unstructured":"[1] E. 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