en تخصصي Special پژوهشي Research <div style="text-align: justify;"><span style="font-size:14px;"><span style="font-family:Source Sans Pro;"><span style="line-height:normal"><span lang="EN-US">This study aimed to examine and validate the consistency and predictive patterns of human-led undergraduate admissions decisions through the application of machine learning models. Unlike traditional holistic evaluation processes conducted by human assessors, this study compared five machine learning algorithms &minus; Gradient Boosting, Random Forest, Support Vector Machine, Logistic Regression, and XGBoost &minus; to identify the most accurate prediction model. The analysis utilized a dataset of 1,554 application records from the 2024 application cycle. To further improve prediction accuracy, Latent Dirichlet Allocation (LDA) was utilized to extract relevant features from unstructured textual data. The findings revealed that the XGBoost model performed best in predicting admission outcomes. This result is attributed to the learning mechanisms of tree-based ensemble models, which is capable of capturing the complex interactions between non-linear score patterns and various others variables. Major factors influencing admission decisions encompassed interview scores, type of application, and document evaluation scores, highlighting their significance in the selection process and validating the effectiveness of the XGBoost as a supportive tool. These findings not only provide practical recommendations for improving prediction accuracy but also inform future research directions in data-driven strategies for high-stakes educational assessment.</span></span></span></span></div> Holistic Undergraduate Admissions Review Process, Latent Dirichlet Allocation (LDA), XGBoost, Machine Learning, Educational Data Mining, Prediction Accuracy 92 110 http://johepal.com/browse.php?a_code=A-10-1650-2&slc_lang=en&sid=1 Yousun Shin 10031947532846004749 10031947532846004749 Yes Sunchon National University Hee Sun Kang 10031947532846004750 10031947532846004750 No Sunchon National University So Yun Park 10031947532846004751 0009-0000-7980-915X No Sunchon National University