Factors that Influence Student Retention

Abstract

[EN] With the increase in enrolment figures from second level education to third level education over the last number of decades, non-progression rates continue to give cause for concern in certain levels and disciplines. It has been widely argued that in addition to increasing enrolment numbers, higher education must also be concerned with the success of these students. In both the Irish and the international sector, the negative consequences of non-progression has been highlighted, not just on a societal level, but also for the students themselves. It is crucial for first-year student experience to have a positive experience and be fully supported in achieving the goals of higher education. From researching several reports in the area of retention and in particular the reports published by the Irish Higher Education Authority and the National Forum for the Enhancement of Teaching and Learning in Higher Education in this area, it is clear that there is a need to analyse the data available and present the findings in a clear way to the key decision makers to allow for early intervention. This paper uses the different phases of the CRISP-DM methodology and applies data mining techniques and models to a real student dataset with the aim to predict the students that will progress. Keywords: Learning analytics; Data Mining; Higher Education; Retention.