Prediction of Admission Tests for Medical Students’ Academic Performance

Althewini, Abdulaziz; Albaz, Noof

doi:10.2147/amep.s355474

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…The study found a significant positive association between pre-admission student scores and their academic performance [ 9 ]. Similar studies found that GAT and SAAT have the strongest relationship to student performance in medical college in Saudi Arabia [ 10 ]. Other studies of Clinical Biochemistry students at King Adulaziz University showed a weak correlation between SAAT and student performance [ 11 ].…”

Section: Introductionmentioning

confidence: 61%

Investigating Students’ Pre-University Admission Requirements and Their Correlation with Academic Performance for Medical Students: An Educational Data Mining Approach

2023

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Medical education is one of the most sought-after disciplines for its prestigious and noble status. Institutions endeavor to identify admissions criteria to register bright students who can handle the complexity of medical training and become competent clinicians. This study aims to apply statistical and educational data mining approaches to study the relationship between pre-admission criteria and student performance in medical programs at a public university in Saudi Arabia. The present study is a retrospective cohort study conducted at the College of Computer Science, King Khalid University, Abha, Kingdom of Saudi Arabia between February and November 2022. The current pre-admission criterion is the admission score taken as the weighted average of high school percentage (HSP), general aptitude test (GAT) and standard achievement admission test (SAAT), with respective weights of 0.3, 0.3 and 0.4. Regression and optimization techniques have been applied to identify weightages that better fit the data. Five classification techniques—Decision Tree, Neural Network, Random Forest, Naïve Bayes and K-Nearest Neighbors—are employed to develop models to predict student performance. The regression and optimization analyses show that optimized weights of HSP, GAT and SAAT are 0.3, 0.2 and 0.5, respectively. The results depict that the performance of the models improves with admission scores based on optimized weightages. Further, the Neural Network and Naïve Bayes techniques outperform other techniques. Firstly, this study proposes to revise the weights of HSP, GAT and SAAT to 0.3, 0.2 and 0.5, respectively. Secondly, as the evaluation metrics of models remain less than 0.75, this study proposes to identify additional student features for calculating admission scores to select ideal candidates for medical programs.

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Section: Introductionmentioning

confidence: 61%

Investigating Students’ Pre-University Admission Requirements and Their Correlation with Academic Performance for Medical Students: An Educational Data Mining Approach

2023

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“…Prior studies have also demonstrated an association between academic performance and admission test scores. These studies concluded that admission test scores can be reliable predictors of academic success and the likelihood of graduation [ 3 , 6 , 20 , 21 ]. However, it is important to take into consideration that test performance can be affected by a magnitude of factors.…”

Section: Discussionmentioning

confidence: 99%

Predicting Success: A Comprehensive Analysis of High School and Admission Test Scores on Future Academic Performance of Dental Students

Iqbal,

Kiyani,

Niazi

et al. 2024

Cureus

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Introduction Dental school admissions in Pakistan traditionally rely on Higher Secondary School Certificate (HSSC), University of Health Sciences (UHS), and National Testing Service (NTS) scores, with limited research available on their predictive validity for dental school performance. This study aims to investigate the correlation between a student's first-year dental school performance and their HSSC, UHS, and NTS scores. Methods A total of 282 records, spanning the years 2016 to 2020, were obtained from a single private dental institution. The data included HSSC, UHS, and/or NTS scores, with the first professional examination results as the dependent variable. Statistical analysis was conducted using the Statistical Package for the Social Sciences (IBM SPSS Statistics for Windows, IBM Corp., Version 25.0, Armonk, NY), encompassing descriptive statistics, Pearson's correlation coefficients, and multiple regression analysis. Results Pearson's coefficients revealed weak to moderate positive correlations between the first professional examination and HSSC (r=0.209, p<.01), UHS (r=0.344, p<.01), and NTS (r=0.350, p<.01), all statistically significant at p < 0.01. Multiple regression analysis indicated that UHS scores contributed the highest explanatory power (R² = 0.146) in predicting first professional examination results. Conclusion A positive correlation between HSSC, UHS, and NTS scores with dental students' performance in the first professional examination is observed. However, the correlations are moderate, highlighting the importance of incorporating assessments that consider cognitive, behavioral, and skill-related aspects in admissions processes. Given the evolving landscape of dental education, these findings underscore the need for a holistic approach to identify candidates better equipped to serve the healthcare sector.

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Prediction of Admission Tests for Medical Students’ Academic Performance

Cited by 2 publications

References 18 publications

Investigating Students’ Pre-University Admission Requirements and Their Correlation with Academic Performance for Medical Students: An Educational Data Mining Approach

Investigating Students’ Pre-University Admission Requirements and Their Correlation with Academic Performance for Medical Students: An Educational Data Mining Approach

Predicting Success: A Comprehensive Analysis of High School and Admission Test Scores on Future Academic Performance of Dental Students

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