A data-driven approach to predict first-year students’ academic success in higher education institutions
| Autor: | Paulo Diniz Gil, Susana da Cruz Martins, Joana Martinho Costa, Sérgio Moro |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Ciências Sociais::Ciências da Educação [Domínio/Área Científica]
Higher education Computer science media_common.quotation_subject SVM Academic success Library and Information Sciences Bachelor Modelling Education Data-driven Data modeling 0502 economics and business Mathematics education Set (psychology) Data mining media_common business.industry 05 social sciences Educational technology 050301 education Ciências Naturais::Ciências da Computação e da Informação [Domínio/Área Científica] language.human_language language Predictive power 050211 marketing Portuguese business Sensitivity analysis 0503 education |
| Zdroj: | Repositório Científico de Acesso Aberto de Portugal Repositório Científico de Acesso Aberto de Portugal (RCAAP) instacron:RCAAP |
| Popis: | 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. info:eu-repo/semantics/acceptedVersion |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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