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.
Highlights
The concept of academic success, which is pivotal to an analytical tool for assessing the quality of HEIs, has several problems in its definition and, in its operationalization (York et al, 2015)
This study aims to apply data mining techniques to an academic data set provided by a Portuguese Higher Institution, and present meaningful information to increase academic success rate
This concept has a myriad of meanings and very diverse uses, depending on the various scientific approaches, and on its recognition in the various systems and public policies of higher education systems, and the practices and cultures prevailing in educational institutions
Summary
The concept of academic success, which is pivotal to an analytical tool for assessing the quality of HEIs, has several problems in its definition and, in its operationalization (York et al, 2015). This concept has a myriad of meanings and very diverse uses, depending on the various scientific approaches, and on its recognition in the various systems and public policies of higher education systems, and the practices and cultures prevailing in educational institutions. Students participate in external commitments, such as family, work and community These set of features is being used as root to correlation and patterns studies regarding academic success
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