A data-driven approach to predict first-year students’ academic success in higher education institutions

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,...

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Bibliographic Details
Main Author: Gil, P. D. (author)
Other Authors: Martins, S. C. (author), Moro, S. (author), Costa, J. M. (author)
Format: article
Language:eng
Published: 2021
Subjects:
Academic success
Data mining
Higher education
Modelling
SVM
Sensitivity analysis
Online Access:http://hdl.handle.net/10071/21296
Country:Portugal
Oai:oai:repositorio.iscte-iul.pt:10071/21296

Internet

http://hdl.handle.net/10071/21296

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