{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T07:29:52Z","timestamp":1766388592999,"version":"3.48.0"},"reference-count":17,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T00:00:00Z","timestamp":1766102400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>Advancements in data storage and data processing technologies has compelled higher education institutions to optimise the use of their data. Many universities globally have begun to implement learning analytics at their institutions to better understand and improve teaching and learning. African higher education institutions have been slow to implement learning analytics despite the continued accumulation of digital data. The research related to this study presents a dataset of Information Systems and Technology (IS&amp;T) students from the University of KwaZulu-Natal, a South African university. The dataset comprises approximately 14,000 registered student records from 10 IS&amp;T courses, primarily consisting of demographic data, academic performance (including past IS&amp;T courses and school records), and Learning Management System (LMS) interaction data. The dataset exhibits an imbalance, characterised by a higher proportion of students who have successfully completed courses compared to those who have not. The dataset will be of interest to researchers engaged in learning analytics application studies, including early pass\/fail prediction and grade classification, as well as those who want to test their techniques on a real-world dataset.<\/jats:p>","DOI":"10.3390\/data11010001","type":"journal-article","created":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T07:01:33Z","timestamp":1766386893000},"page":"1","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Anonymized Dataset of Information Systems and Technology Students at a South African University for Learning Analytics"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6010-1431","authenticated-orcid":false,"given":"Rushil","family":"Raghavjee","sequence":"first","affiliation":[{"name":"Discipline of Computer Science, University of KwaZulu-Natal, Pietermaritzburg 3201, South Africa"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2719-3503","authenticated-orcid":false,"given":"Prabhakar Rontala","family":"Subramaniam","sequence":"additional","affiliation":[{"name":"Discipline of Computer Science, University of KwaZulu-Natal, Pietermaritzburg 3201, South Africa"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4499-1091","authenticated-orcid":false,"given":"Irene","family":"Govender","sequence":"additional","affiliation":[{"name":"Discipline of Computer Science, University of KwaZulu-Natal, Pietermaritzburg 3201, South Africa"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,19]]},"reference":[{"key":"ref_1","unstructured":"Siemens, G., Gasevic, D., Haythornthwaite, C., Dawson, S., Shum, S.B., Ferguson, R., Duval, E., Verbert, K., and Baker, R. (2011). Open Learning Analytics: An Integrated & Modularized Platform, Open University Press Maidenhead."},{"key":"ref_2","unstructured":"SoLAR (2025, September 10). SoLAR: What Is Learning Analytics?. Available online: https:\/\/www.solaresearch.org\/about\/what-is-learning-analytics\/."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"218","DOI":"10.18608\/jla.2022.7539","article-title":"Learning Analytics on the African Continent: An Emerging Research Focus and Practice","volume":"9","author":"Prinsloo","year":"2022","journal-title":"J. Learn. Anal."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Perai\u0107, I., Grubi\u0161i\u0107, A., and Pintari\u0107, N. (2025, January 23\u201325). Machine Learning in Learning Analytics Dashboards: A Systematic Literature Review. 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Data Min."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"329","DOI":"10.22214\/ijraset.2020.5055","article-title":"A Prediction Model for Student Attrition Using J48 Classification","volume":"8","author":"Ribot","year":"2020","journal-title":"Int. J. Res. Appl. Sci. Eng. Technol. (IJRASET)"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6","DOI":"10.18608\/jla.2014.11.3","article-title":"Early Alert of Academically At-Risk Students: An Open Source Analytics Initiative","volume":"1","author":"Jayaprakash","year":"2014","journal-title":"J. Learn. Anal."},{"key":"ref_13","first-page":"89","article-title":"Causes and Structural Effects of Student Absenteeism: A Case Study of Three South African Universities","volume":"26","author":"Wadesango","year":"2011","journal-title":"J. Soc. 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