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

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Bibliographic Details
Title: Early Detection of At-Risk Undergraduate Students through Academic Performance Predictors
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
FullText Text:
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  Data: Early Detection of At-Risk Undergraduate Students through Academic Performance Predictors
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  Data: 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
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  Data: <searchLink fieldCode="DE" term="%22Mauritania%22">Mauritania</searchLink>
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  Label: Abstract
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  Data: 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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      – Text: English
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      Pagination:
        PageCount: 13
        StartPage: 42
    Subjects:
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Undergraduate Students
        Type: general
      – SubjectFull: Identification
        Type: general
      – SubjectFull: At Risk Students
        Type: general
      – SubjectFull: Low Achievement
        Type: general
      – SubjectFull: Academic Failure
        Type: general
      – SubjectFull: Predictor Variables
        Type: general
      – SubjectFull: Predictive Measurement
        Type: general
      – SubjectFull: Early Intervention
        Type: general
      – SubjectFull: Potential Dropouts
        Type: general
      – SubjectFull: Dropout Prevention
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      – SubjectFull: Personality Traits
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      – SubjectFull: Cognitive Style
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      – SubjectFull: Socioeconomic Status
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      – SubjectFull: Learner Engagement
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      – SubjectFull: Demography
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      – SubjectFull: Likert Scales
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      – SubjectFull: Correlation
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      – SubjectFull: Factor Analysis
        Type: general
      – SubjectFull: Componential Analysis
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      – SubjectFull: Grade Point Average
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      – SubjectFull: Student Surveys
        Type: general
      – SubjectFull: Multiple Regression Analysis
        Type: general
      – SubjectFull: Monte Carlo Methods
        Type: general
      – SubjectFull: Mauritania
        Type: general
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      – TitleFull: Early Detection of At-Risk Undergraduate Students through Academic Performance Predictors
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