Early Detection of At-Risk Undergraduate Students through Academic Performance Predictors

Abstract

Undergraduate student dropout is gradually becoming a global problem and the 39 Small Islands Developing States (SIDS) are no exception to this trend. The purpose of this research was to develop a method that can be used for early detection of students who are at-risk of performing poorly in their undergraduate studies. A sample of 279 students participated in the study conducted in a Mauritian private tertiary academic institution. Results of regression analyses identified the variables having a significant influence on academic performance. These variables were used in a linear discriminant analysis where 74 percent of the students could be correctly classified into three categories: at-risk, pass or fail. In conclusion, this study has proposed a new technique that can be used by institutions to determine significant academic performance predictors and then identify at-risk students upon whom interventions can be implemented prior to exams to address the problem of dropouts.

Predicción de deserción y éxito en estudiantes. Caso de estudio: ingeniería agroindustrial de la UNI Norte, Nicaragua, 2011-2015

“…01, pp. 16-27 / Junio 2018 estudiantes de pregrado, categorizándolos en tres categorías: aprobado, reprobado y en riesgo (Rowtho, 2017).…”