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Challenges of Student Selection: Predicting Academic Performance

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Title: Challenges of Student Selection: Predicting Academic Performance
Language: English
Authors: van der Merwe, D., de Beer, M.
Source: South African Journal of Higher Education. 2006 20(4):547-562.
Availability: Unisa Press. Preller Street, P.O. Box 392, Muckleneuk, Pretoria 0003, South Africa. Tel: +27-24-298960; Fax: +27-24-293449; e-mail: sajhe@vodamail.co.za; Web site: http://www.sajhe.org.za
Peer Reviewed: Y
Page Count: 16
Publication Date: 2006
Document Type: Journal Articles
Reports - Evaluative
Education Level: Higher Education
Descriptors: Disadvantaged, Academic Achievement, Predictive Validity, Admission Criteria, Psychometrics, Cognitive Ability, Robustness (Statistics), Goodness of Fit, Scoring Rubrics, Evaluation Problems, Longitudinal Studies, Predictor Variables, Correlation
Geographic Terms: South Africa
ISSN: 1011-3487
Abstract: Finding accurate predictors of tertiary academic performance, specifically for disadvantaged students, is essential because of budget constraints and the need of the labour market to address employment equity. Increased retention, throughput and decreased dropout rates are vital. When making admission decisions, the under preparedness of students necessitates that their potential cognitive abilities should be assessed rather than their current abilities. In predicting their academic performance, it is argued that conventional psychometric tests are less suitable for the selection of students from disadvantaged backgrounds, because they are a static measure of current abilities which gives no indication of the student's potential to learn when in an optimum environment. The predictive validity of the Potential Index Battery, the Learning Potential Computerised Adaptive Test and school-leaving results in selection, were determined by calculating the correlation of these measures with academic performance over the full duration of the students' studies. Statistically significant correlations were found, thus indicating that the learning potential test had higher predictive powers than static measures of cognitive ability and school-leaving results, in predicting future academic performance. (Contains 6 tables and 2 notes.)
Abstractor: As Provided
Number of References: 45
Entry Date: 2009
Access URL: https://www.sajhe.org.za/
Accession Number: EJ852714
Database: ERIC
FullText Text:
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  Data: Challenges of Student Selection: Predicting Academic Performance
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  Data: <searchLink fieldCode="AR" term="%22van+der+Merwe%2C+D%2E%22">van der Merwe, D.</searchLink><br /><searchLink fieldCode="AR" term="%22de+Beer%2C+M%2E%22">de Beer, M.</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22South+African+Journal+of+Higher+Education%22"><i>South African Journal of Higher Education</i></searchLink>. 2006 20(4):547-562.
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  Data: Unisa Press. Preller Street, P.O. Box 392, Muckleneuk, Pretoria 0003, South Africa. Tel: +27-24-298960; Fax: +27-24-293449; e-mail: sajhe@vodamail.co.za; Web site: http://www.sajhe.org.za
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  Data: Journal Articles<br />Reports - Evaluative
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  Data: Finding accurate predictors of tertiary academic performance, specifically for disadvantaged students, is essential because of budget constraints and the need of the labour market to address employment equity. Increased retention, throughput and decreased dropout rates are vital. When making admission decisions, the under preparedness of students necessitates that their potential cognitive abilities should be assessed rather than their current abilities. In predicting their academic performance, it is argued that conventional psychometric tests are less suitable for the selection of students from disadvantaged backgrounds, because they are a static measure of current abilities which gives no indication of the student's potential to learn when in an optimum environment. The predictive validity of the Potential Index Battery, the Learning Potential Computerised Adaptive Test and school-leaving results in selection, were determined by calculating the correlation of these measures with academic performance over the full duration of the students' studies. Statistically significant correlations were found, thus indicating that the learning potential test had higher predictive powers than static measures of cognitive ability and school-leaving results, in predicting future academic performance. (Contains 6 tables and 2 notes.)
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      – Text: English
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      Pagination:
        PageCount: 16
        StartPage: 547
    Subjects:
      – SubjectFull: Disadvantaged
        Type: general
      – SubjectFull: Academic Achievement
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      – SubjectFull: Predictive Validity
        Type: general
      – SubjectFull: Admission Criteria
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      – SubjectFull: Psychometrics
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      – SubjectFull: Cognitive Ability
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      – SubjectFull: Robustness (Statistics)
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      – SubjectFull: Goodness of Fit
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      – SubjectFull: Scoring Rubrics
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      – SubjectFull: Evaluation Problems
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      – SubjectFull: Longitudinal Studies
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      – SubjectFull: Predictor Variables
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      – SubjectFull: Correlation
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      – SubjectFull: South Africa
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      – TitleFull: Challenges of Student Selection: Predicting Academic Performance
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