# Beyond admission scores: evaluating GAT, SAAT, and English proficiency as predictors of physics performance in health science education

## TL;DR

This study examines how well standardized tests and English proficiency predict physics performance in Saudi health science students, finding limited predictive power.

## Contribution

The study introduces a novel evaluation of GAT, SAAT, and English proficiency as predictors of physics performance in health science education.

## Key findings

- Reading proficiency was the strongest predictor of physics grades, explaining 19.6% of the variance.
- The combined model of GAT, SAAT, and English metrics explained 29.3% of physics performance variance.
- Non-cognitive factors may significantly influence academic outcomes but are not captured by current admissions criteria.

## Abstract

Accurate prediction of academic performance is essential for selecting students into competitive medical and health science programs. In Saudi Arabia, standardized cognitive assessments—such as the General Aptitude Test (GAT) and Scholastic Achievement Admission Test (SAAT)—are widely used for admissions. This study evaluates the predictive validity of these tests, alongside English proficiency measures, in forecasting student performance in an introductory physics course—a foundational subject in science-based programs.

This retrospective, quantitative study analyzed data from 250 Saudi college students enrolled in a required introductory physics course. Predictor variables included GAT scores (critical thinking and reasoning), SAAT scores (content knowledge in science and math), and English proficiency, assessed via three metrics: preparatory-year English course average, reading test scores, and communication skills test scores. Both simple linear regression and multiple regression analyses were conducted to evaluate the individual and combined predictive contributions of these variables to final physics course grades.

All predictors were statistically significant. Among them, reading proficiency was the strongest individual predictor, accounting for 19.6% of the variance in physics grades, followed by GAT (9.4%) and SAAT (7.9%). Communication test scores explained a smaller portion (7.2%). The combined model explained 29.3% of the total variance in physics performance, leaving approximately 70% of the variance unexplained by the selected cognitive measures.

Although GAT, SAAT, and English reading proficiency contribute modestly to predicting physics course performance, their limited combined predictive power points to the need for more comprehensive admissions criteria. Non-cognitive factors—such as motivation, study habits, or self-efficacy—may significantly influence academic outcomes but remain unmeasured in current systems. These findings support calls for reforming admissions practices in Saudi health science education to adopt a more holistic and evidence-informed approach.