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Predicting Performance in Higher Education Using Proximal Predictors

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  • A Susan M Niessen
  • Rob R Meijer
  • Jorge N Tendeiro

Abstract

We studied the validity of two methods for predicting academic performance and student-program fit that were proximal to important study criteria. Applicants to an undergraduate psychology program participated in a selection procedure containing a trial-studying test based on a work sample approach, and specific skills tests in English and math. Test scores were used to predict academic achievement and progress after the first year, achievement in specific course types, enrollment, and dropout after the first year. All tests showed positive significant correlations with the criteria. The trial-studying test was consistently the best predictor in the admission procedure. We found no significant differences between the predictive validity of the trial-studying test and prior educational performance, and substantial shared explained variance between the two predictors. Only applicants with lower trial-studying scores were significantly less likely to enroll in the program. In conclusion, the trial-studying test yielded predictive validities similar to that of prior educational performance and possibly enabled self-selection. In admissions aimed at student-program fit, or in admissions in which past educational performance is difficult to use, a trial-studying test is a good instrument to predict academic performance.

Suggested Citation

  • A Susan M Niessen & Rob R Meijer & Jorge N Tendeiro, 2016. "Predicting Performance in Higher Education Using Proximal Predictors," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-14, April.
  • Handle: RePEc:plo:pone00:0153663
    DOI: 10.1371/journal.pone.0153663
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    Cited by:

    1. Cédric Beaulac & Jeffrey S. Rosenthal, 2019. "Predicting University Students’ Academic Success and Major Using Random Forests," Research in Higher Education, Springer;Association for Institutional Research, vol. 60(7), pages 1048-1064, November.
    2. Jessica Findley & Adriana Cimetta & Heidi Legg Burross & Katherine C. Cheng & Matt Charles & Cayley Balser & Ran Li & Christopher Robertson, 2023. "JD‐Next: A valid and reliable tool to predict diverse students' success in law school," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 20(1), pages 134-165, March.

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