Abstract
University dropout is a growing problem with considerable academic, social and economic consequences. Conclusions and limitations of previous studies highlight the difficulty of analyzing the phenomenon from a broad perspective and with bigger data sets. This paper proposes a new, machine-learning based method, able to examine the problem using a holistic approach. Advantages of this method include the lack of strong distribution hypothesis, the capacity for handling bigger data sets and the interpretability of the results. Results are consistent with previous research, showing the influence of personal and contextual variables and the importance of academic performance in the first year, but other factors are also highlighted with this model, such as the importance of dedication (part or full time), and the vulnerability of the students with respect to their age. Additionally, a comprehensive graphic output is included to make it easier to interpret the discovered rules.
Highlights
Governments have promoted the democratization of access to higher education systems, and this broader access has increased interest in dropout at the university level
The machine learning (ML) rules extracted which predict dropout share a common core of factors with those produced by statistical methods in previous research, but this current study introduces new factors and highlights their importance in comparison with previous models
We saw the importance of performance, but while other studies were much more focused on prior grades [11,17], in our model the most important determining factor is the performance in the first year. This result underlines the importance of the first year in university and challenges higher education institutions to pay special attention to performance in students’ first semesters in order to adopt preventive measures
Summary
Governments have promoted the democratization of access to higher education systems, and this broader access has increased interest in dropout at the university level. Research on this topic grew in parallel with the development of statistical techniques and computing capacity. Researchers must remember that different definitions of dropout have been applied. Throughout this paper, we use the definition of dropout used in the Spanish context, described by the National Agency for Quality, acknowledging students as having withdrawn when, after being registered in a particular program, they do not enroll again for the following two years. In Spain, the European Higher Education Area and University 2020 framework have led to a major concern in universities regarding student affairs services.
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