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Investigating Students' Pre-University Admission Requirements and Their Correlation with Academic Performance for

Ayman Qahmash1, Naim Ahmad1, Abdulmohsen Algarni2

  • 1Department of Information Systems, King Khalid University, Alfara, Abha 61421, Saudi Arabia.

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|March 29, 2023
PubMed
Summary

Optimizing admission criteria for medical programs by adjusting weights for high school percentage, general aptitude test, and achievement test improves student performance prediction. Further research is needed to identify additional features for ideal candidate selection.

Keywords:
Saudi public universityclassificationeducational data miningmedical educationpre-admission criteriastudent performance

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Area of Science:

  • Educational Data Mining
  • Statistical Analysis
  • Medical Education

Background:

  • Medical education admissions seek high-achieving students for complex training.
  • Current admission scores use weighted averages of High School Percentage (HSP), General Aptitude Test (GAT), and Standard Achievement Admission Test (SAAT).

Purpose of the Study:

  • To apply statistical and educational data mining to analyze pre-admission criteria and student performance in Saudi Arabian medical programs.
  • To identify optimal weightages for admission criteria to enhance student performance prediction.

Main Methods:

  • Retrospective cohort study at King Khalid University (February-November 2022).
  • Regression and optimization techniques to determine new weightages for HSP, GAT, and SAAT.
  • Five classification techniques (Decision Tree, Neural Network, Random Forest, Naïve Bayes, K-Nearest Neighbors) were used to predict student performance.

Main Results:

  • Optimized weights for HSP, GAT, and SAAT were found to be 0.3, 0.2, and 0.5, respectively.
  • Admission scores using optimized weights improved model performance.
  • Neural Network and Naïve Bayes models demonstrated superior predictive capabilities.

Conclusions:

  • Revising admission criteria weights to 0.3 (HSP), 0.2 (GAT), and 0.5 (SAAT) is recommended.
  • Exploring additional student features is suggested to further enhance the selection of ideal candidates, as current model metrics are below 0.75.