Quantifying Holistic Review: A Multi-Modal Approach to College Admissions Prediction

TL;DR

This paper presents CAPS, a multi-modal, interpretable framework for college admissions prediction that combines academic, essay, and extracurricular data using advanced machine learning techniques, improving transparency and consistency.

Contribution

The paper introduces CAPS, a novel multi-modal framework that models and interprets holistic admissions evaluations with high accuracy and explainability.

Findings

Achieved an EQI prediction R^2 of 0.80

Classification accuracy over 75%

Macro F1 score of 0.69

Abstract

This paper introduces the Comprehensive Applicant Profile Score (CAPS), a novel multi-modal framework designed to quantitatively model and interpret holistic college admissions evaluations. CAPS decomposes applicant profiles into three interpretable components: academic performance (Standardized Academic Score, SAS), essay quality (Essay Quality Index, EQI), and extracurricular engagement (Extracurricular Impact Score, EIS). Leveraging transformer-based semantic embeddings, LLM scoring, and XGBoost regression, CAPS provides transparent and explainable evaluations aligned with human judgment. Experiments on a synthetic but realistic dataset demonstrate strong performance, achieving an EQI prediction R^2 of 0.80, classification accuracy over 75%, a macro F1 score of 0.69, and a weighted F1 score of 0.74. CAPS addresses key limitations in traditional holistic review -- particularly the opacity, inconsistency, and anxiety faced by applicants -- thus paving the way for more equitable and data-informed admissions practices.