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Gil, P. D., Martins, S. C., Moro, S. & Costa, J. M. (2021). A data-driven approach to predict first-year students’ academic success in higher education institutions. Education and Information Technologies. 26 (2), 2165-2190

P. D. Gil et al.,  "A data-driven approach to predict first-year students’ academic success in higher education institutions", in Education and Information Technologies, vol. 26, no. 2, pp. 2165-2190, 2021

@article{gil2021_1714104856368,
	author = "Gil, P. D. and Martins, S. C. and Moro, S. and Costa, J. M.",
	title = "A data-driven approach to predict first-year students’ academic success in higher education institutions",
	journal = "Education and Information Technologies",
	year = "2021",
	volume = "26",
	number = "2",
	doi = "10.1007/s10639-020-10346-6",
	pages = "2165-2190",
	url = "https://www.springer.com/journal/10639"
}

TY  - JOUR
TI  - A data-driven approach to predict first-year students’ academic success in higher education institutions
T2  - Education and Information Technologies
VL  - 26
IS  - 2
AU  - Gil, P. D.
AU  - Martins, S. C.
AU  - Moro, S.
AU  - Costa, J. M.
PY  - 2021
SP  - 2165-2190
SN  - 1360-2357
DO  - 10.1007/s10639-020-10346-6
UR  - https://www.springer.com/journal/10639
AB  - 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.
ER  -