Document Type : Research Paper

Authors

1 Ph.D. Student in Assessment & Measurement, University of Tehran, Tehran, Iran.

2 Professor, Department of Curriculum Development and Instruction Methods, University of Tehran, Tehran, Iran.

3 Associate Professor, Department of Educational Psychology, University of Tehran, Tehran, Iran.

Abstract

Ranking candidates in Iran’s National University Entrance Exam (Konkoor) is a critical step in the university admission process. Traditional ranking methods, based on a linear combination of standardized scores, face limitations such as neglecting data distribution and inter-variable correlations. This study aims to enhance the ranking process by employing Principal Component Analysis (PCA) for dimensionality reduction and Mahalanobis Distance for candidate ranking. The study population consisted of 126,728 candidates from the Mathematics and Technical Sciences group in the 2021 exam, reduced to 110,550 after eliminating missing data. Data were preprocessed, subjected to PCA for dimensionality reduction, and ranked using Mahalanobis Distance. Analyses were conducted using SPSS and Python. The results indicate that the proposed method offers greater discriminatory precision in distinguishing among candidates while retaining critical informational variance. This enhanced approach facilitates a more effective identification of academically superior candidates for university admission. The findings suggest that integrating Principal Component Analysis and the Mahalanobis Distance provides a robust and more effective methodological framework for candidate ranking, with the potential to significantly improve upon current admission methodologies.

Keywords

سازمان سنجش آموزش کشور. (۱۴۰۰). آشنایی با محتوای کارنامه نتایج علمی آزمون سراسری سال ۱۴۰۰. مرداد ۱۴۰۰.
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