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

Vikash Rowtho

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.


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DOI: https://doi.org/10.5539/hes.v7n3p42

Copyright (c) 2017 Vikash Rowtho

License URL: http://creativecommons.org/licenses/by/4.0

Higher Education Studies  ISSN 1925-4741 (Print)   ISSN 1925-475X (Online)

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