Early Detection of At-Risk Undergraduate Students through Academic Performance Predictors
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| Title: | Early Detection of At-Risk Undergraduate Students through Academic Performance Predictors |
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| Language: | English |
| Authors: | Rowtho, Vikash |
| Source: | Higher Education Studies. 2017 7(3):42-54. |
| Availability: | Canadian Center of Science and Education. 1120 Finch Avenue West Suite 701-309, Toronto, OH M3J 3H7, Canada. Tel: 416-642-2606; Fax: 416-642-2608; e-mail: hes@ccsenet.org; Web site: http://www.ccsenet.org/journal/index.php/hes |
| Peer Reviewed: | Y |
| Page Count: | 13 |
| Publication Date: | 2017 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Foreign Countries, Undergraduate Students, Identification, At Risk Students, Low Achievement, Academic Failure, Predictor Variables, Predictive Measurement, Early Intervention, Potential Dropouts, Dropout Prevention, Personality Traits, Cognitive Style, Socioeconomic Status, Learner Engagement, Demography, Likert Scales, Correlation, Factor Analysis, Componential Analysis, Grade Point Average, Student Surveys, Multiple Regression Analysis, Monte Carlo Methods |
| Geographic Terms: | Mauritania |
| ISSN: | 1925-4741 |
| 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. |
| Abstractor: | As Provided |
| Number of References: | 55 |
| Entry Date: | 2017 |
| Accession Number: | EJ1150071 |
| Database: | ERIC |
| 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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| ISSN: | 1925-4741 |
