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Cited by 51Online Learning and AnalyticsHigher Education Research StudiesData Mining and Machine Learning Applications

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

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Gil, Paulo Diniz, da Cruz Martins, Susana, Moro, Sérgio, Costa, Joana Martinho·2021·Education and Information Technologies
DOI 10.1007/s10639-020-10346-6
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Citations

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Research works printed but not linked: 40

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Metadata

Authors
Gil, P. D., da Cruz Martins, S., Moro, S., & Costa, J. M.
Affiliations
Iscte – Instituto Universitário de Lisboa (PT)
Corresponding author
Moro, Sérgio
ORCID
Venue
Education and Information Technologies
Publisher
Springer Science and Business Media LLC
Published
2020-10-06
Volume
26
Issue
2
Pages
2165-2190
ISSN
  • 1360-2357 (ISSN-L)
  • 1573-7608
DOI
10.1007/s10639-020-10346-6
OpenAlex
W3090209840
Cited by
51
References
3
Citations by year
2026: 6 · 2025: 9 · 2024: 16 · 2023: 5 · 2022: 11 · 2021: 4
FWCI
4.56
Type
journal article
Language
en
Open access
Licence
Locations
  • Education and Information Technologies (publishedVersion)
  • Repositório do ISCTE-IUL (submittedVersion, cc-by-nc-nd)
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
  • Social Sciences › Education › Higher Education Research Studies
  • Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
Keywords
academic success prediction, first-year students, school dropout, higher education, data mining, support vector machine, feature selection, sociodemographic factors, academic performance, student monitoring, data-driven decision making

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