Prediction of Standardized Tests and English Competence for Saudi Medical Students’ Performance in an Introductory Physics Course
Bibliographic record
Abstract
The following study tested the relationship between admission criteria and college students’ performance in an introductory physics course. For this study, I analyzed the performance of 250 students based on two college admission standardized tests (i.e., General Aptitude Test (GAT), Scholastic Achievement Admission Test (SAAT)), and English competence performance (i.e., average English course grades and reading and communication proficiency test). Based on this analysis, GAT and SAAT, along with English competence, are significant individual predictors for students’ performance in physics. Reading proficiency tests were the best individual predictors in simple linear regression analysis with 19.6% variance. The combined methods, with multivariate regression analysis, explained only 29.3% of physics course grade variance. This low variance of Saudi admission criteria for a single physics course should motivate Saudi policymakers to conduct a national study that includes an increased number of participants. Through such a national study, more evidence-based conclusions regarding the college admission system can be made to improve the admission process for Saudi students.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".