Predictive validity of pre-admission assessments on medical student performance

Dabaliz, Al-Awwab M.; Kaadan, Samy; Dabbagh, M. Marwan; Barakat, Abdulaziz; Shareef, Mohammad Abrar; Al-Tannir, Mohamad; Obeidat, Akef; Mohamed, Ayman M.A.

doi:10.5116/ijme.5a10.04e1

Cited by 20 publications

(22 citation statements)

References 31 publications

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“…More weight should therefore be assigned to it. This result matches those of other national studies conducted in KSA (e.g., [11], [12], [15], [16]), which also show that SAAT score is the most significant positive predictor of student performance in Saudi medical schools. However, our result contradicts what is proposed in another national study [14], which found that the HSGA affects student CGPA more than the other two criteria.…”

Section: ) Evaluation Metricssupporting

confidence: 90%

Using Data Mining Techniques to Predict Student Performance to Support Decision Making in University Admission Systems

Mengash

2020

IEEE Access

186

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An admissions system based on valid and reliable admissions criteria is very important to select candidates likely to perform well academically at institutions of higher education. This study focuses on ways to support universities in admissions decision making using data mining techniques to predict applicants' academic performance at university. A data set of 2,039 students enrolled in a Computer Science and Information College of a Saudi public university from 2016 to 2019 was used to validate the proposed methodology. The results demonstrate that applicants' early university performance can be predicted before admission based on certain pre-admission criteria (high school grade average, Scholastic Achievement Admission Test score, and General Aptitude Test score). The results also show that Scholastic Achievement Admission Test score is the pre-admission criterion that most accurately predicts future student performance. Therefore, this score should be assigned more weight in admissions systems. We also found that the Artificial Neural Network technique has an accuracy rate above 79%, making it superior to other classification techniques considered (Decision Trees, Support Vector Machines, and Naïve Bayes). INDEX TERMS Data mining techniques, educational data mining, performance prediction, pre-admission criteria, student performance.

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Section: ) Evaluation Metricssupporting

confidence: 90%

Using Data Mining Techniques to Predict Student Performance to Support Decision Making in University Admission Systems

Mengash

2020

IEEE Access

186

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“…However the study was done on a very small sample of 91 students. In a bigger study on 737 students the SAAT but not GAT was a positive predictors of cGPA but only in the pre-clinical years and not clinical years (6).…”

Section: Discussionmentioning

confidence: 79%

Relationship between Admission Criteria and Academic Performance in Basic Science Courses in Health Science Colleges in KAU

Alamoudi

Falattah

Eldakhakhny

et al. 2021

Preprint

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Background: In King Abdulaziz University, medical and health science schools depend on admission exams (Aptitude and Achievement) and Preparatory year scores in their students’ selection. However, with the growing number of applicants and the drastic changes in teaching and assessment in these colleges continuous assessment and development of admission criteria are needed. In this study, we aimed to evaluate the correlation of admission exam scores, in addition to the Preparatory year GPA, with academic performance in the basic science subjects such as Clinical Biochemistry and Clinical Pharmacology in health science colleges.Methods: The study was conducted on four cohort studies, two faculty of nursing cohorts; Nursing students (2017-2018,n=146) Nursing students (2018-2019,n=81), and two faculty of applied medical sciences cohorts, Clinical Nutrition students (2017-2018,n=33), Clinical Nutrition students (2018-2019,n=28) .The students’ scores of General Aptitude Test (GAT), Scholastic Achievement Admission Test (SAAT ), and preparatory year GPA were all recorded at the beginning of each semester before the beginning of courses. Clinical Biochemistry and Clinical Pharmacology exam results were recorded at the end of the semester. Correlation was done for each cohort and all cohorts pooled. Results: Results showed only a weak correlation detected between SAAT and the overall achievement in Clinical Biochemistry ( r= 0.192*, P= 0.042) in Nursing students (2017-2018) but no correlation was seen with SAAT or preparatory year scores. There was also no significant correlation between admission exams scores and the students ‘academic achievement in Clinical Pharmacology, and with On the other hand Clinical Pharmacology exam results showed a significant positive correlation with Clinical Biochemistry results (r=0.688, P=0.000). Conclusion: Our results could indicate the need to revisit the admission criteria for these colleges. Furthermore, specific preparatory year tracks for health science colleges can ensure that students improve the specific skills and knowledge required for their future college years.

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“…Dabaliz et al 25 showed that performance in the PT in the preclinical stages of a medical course was not predicted by five variables of the admissions test. 25 In our single dependency study, no correlation between marks in the UAT and in the PT performed at the end of the medical course was observed. However, similarly to OSCE, PT scores helped to increase the correlation between the variables when multivariate analysis was performed.…”

Section: Discussionmentioning

confidence: 99%

“…15 Little is known about the association between marks in admissions tests and in the PT at the end of a medical course. Dabaliz et al 25 showed that performance in the PT in the preclinical stages of a medical course was not predicted by five variables of the admissions test. 25 In our single dependency study, no correlation between marks in the UAT and in the PT performed at the end of the medical course was observed.…”

Section: Discussionmentioning

confidence: 99%

<p>University Admission Test Associates with Academic Performance at the End of Medical Course in a PBL Medical Hybrid Curriculum</p>

Bestetti¹,

Couto²,

Roncato-Paiva³

et al. 2020

AMEP

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Purpose: Most studies assessing the value of the university admissions test (UAT) to predict academic performance at the end of a medical course were carried out on lecture-based medical courses. However, the association between performance in the UAT with academic achievement at the end of medical course in a problem-based learning (PBL) medical hybrid curriculum remains controversial. The aim of this study was to correlate marks in the UAT with those obtained in the Organized Structured Clinical Examination (OSCE), in the progress testing (PT), and in the final marks of the clerkship (FMC). Methods: We used data from 48 medical students. A single and a multiple dependency studies were performed to assess bivariate and multiple correlation between the UAT or the essay scores (dependent variables) and the OSCE, PT, and FMC (independent variables). Pearson test, multiple linear regression, and ANOVA tests were used and a p-value < 0.05 was considered significant. Results: In the bivariate analysis, only the UAT and FMC marks were correlated (r=0.34; p=0.02). However, the multiple dependency study showed a moderate correlation among UAT, OSCE, PT, and FMC marks (r=0.46; p=0.01). No correlation was found between the essay scores and PT, FMC, and OSCE scores. Conclusion: Our study shows that UAT marks, but not essay scores, can predict academic achievement, particularly in terms of clinical competence (FMC) at the end of a medical course in a PBL hybrid curriculum.

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Predictive validity of pre-admission assessments on medical student performance

Cited by 20 publications

References 31 publications

Using Data Mining Techniques to Predict Student Performance to Support Decision Making in University Admission Systems

Using Data Mining Techniques to Predict Student Performance to Support Decision Making in University Admission Systems

Relationship between Admission Criteria and Academic Performance in Basic Science Courses in Health Science Colleges in KAU

<p>University Admission Test Associates with Academic Performance at the End of Medical Course in a PBL Medical Hybrid Curriculum</p>

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