A Data-Driven Approach to Predict First-Year Students' Academic Success in Higher Education Institutions

Autor/inn/en	Gil, Paulo Diniz; da Cruz Martins, Susana; Moro, Sérgio; Costa, Joana Martinho
Titel	A Data-Driven Approach to Predict First-Year Students' Academic Success in Higher Education Institutions
Quelle	In: Education and Information Technologies, 26 (2021) 2, S.2165-2190 (26 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (da Cruz Martins, Susana) ORCID (Moro, Sérgio) ORCID (Costa, Joana Martinho)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1360-2357
DOI	10.1007/s10639-020-10346-6
Schlagwörter	Data Use; Decision Making; Predictor Variables; Academic Achievement; College Freshmen; Student Characteristics; Foreign Countries; Portugal + Suchen Sie Ihr Suchwort? Decision-making; Entscheidungsfindung; Prädiktor; Schulleistung; Studienanfänger; Ausland
Abstract	This study presents a data mining approach to predict academic success of the first-year students. A dataset of 10 academic years for first-year bachelor's degrees from a Portuguese Higher Institution (N = 9652) has been analysed. Features' selection resulted in a characterising set of 68 features, encompassing socio-demographic, social origin, previous education, special statutes and educational path dimensions. We proposed and tested three distinct course stage data models based on entrance date, end of the first and second curricular semesters. A support vector machines (SVM) model achieved the best overall performance and was selected to conduct a data-based sensitivity analysis. The previous evaluation performance, study gaps and age-related features play a major role in explaining failures at entrance stage. For subsequent stages, current evaluation performance features unveil their predictive power. Suggested guidelines include to provide study support groups to risk profiles and to create monitoring frameworks. From a practical standpoint, a data-driven decision-making framework based on these models can be used to promote academic success. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01