An early warning dropout model in higher education degree programs: A case study in Ecuador

Worldwide, a significant concern of universities is to reduce academic dropout rate. Several initiatives have been made to avoid this problem; however, it is essential to recognize at-risk students as soon as possible. In this paper, we propose a new predictive model that can identify the earliest moment of dropping out of a student of any semester in any undergraduate course. Unlike most available models, our solution is based on academic information alone, and our evidence suggests that by ignoring socio-demographics or pre-college entry information, we obtain more reliable predictions, even when a student has only one academic semester finished. Therefore, our prediction can be used as part of an academic counseling tool providing the performance factors that could influence a student to leave the institution. With this, the counselors can identify those students and take better decisions to guide them and finally, minimize the dropout in the institution. As a case study, we used the students" data of all undergraduate programs from 2000 until 2019 from a public high education university in Ecuador.

An early warning dropout model in higher education degree programs: A case study in Ecuador Articles