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

arXiv: https://arxiv.org/abs/2507.15862 · HTML: https://mlsanity.com/paper/2507.15862/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 20 references that are in the map.
- First posted on arXiv 12 Jul 2025.

## Builds on (most cited first, 7 of 7)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [Automated Essay Scoring Using Transformer Models](https://mlsanity.com/paper/2110.06874/) (2021)
- [Evaluating a Learned Admission-Prediction Model as a Replacement for Standardized Tests in College Admissions](https://mlsanity.com/paper/2302.03610/) (2023)
- [Augmenting Holistic Review in University Admission using Natural Language Processing for Essays and Recommendation Letters](https://mlsanity.com/paper/2306.17575/) (2023)
- [Admission Prediction in Undergraduate Applications: an Interpretable Deep Learning Approach](https://mlsanity.com/paper/2401.11698/) (2024)

## Abstract (arXiv)

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.