Academic Analytics Applied in the Study of the Relationship Between the Initial Profile of Undergraduate Students and Early Drop-Out Rates. Defining the Variables of a Predictor Instrument

Abstract

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The field of university dropout research is of utmost importance especially in the current context arising from the Covid-19 pandemic. Students who started their degrees in the last two years completed their pre-university studies during various phases of confinement and by combining traditional and virtual training. In this scenario, students' motivation and the way they cope with the difficulties of their first year of university are very relevant and will depend on a multitude of personal and social variables in their immediate environment. Previous studies have shown that many university students drop out of their studies early, but what factors and to what extent they affect this dropout is still a field under study. This paper focuses on the identification, classification and evaluation of a set of indicators based on teacher and tutor perception in different fields of study by applying quantitative and qualitative techniques. The results of pilot studies developed support the approach adopted, as they show how teachers can identify students at risk of dropping out at the beginning of the course and take proactive measures to monitor and motivate them, thus reducing the possibility of dropout.