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

• URL: http://arxiv.org/abs/2507.15862v2
• Date: Fri, 05 Sep 2025 03:00:48 GMT
• Title: Quantifying Holistic Review: A Multi-Modal Approach to College Admissions Prediction
• Abstract summary: Comprehensive Applicant Profile Score (CAPS) is a novel framework designed to quantitatively model and interpret holistic college admissions evaluations.<n>CAPS decomposes applicant profiles into three interpretable components: academic performance, essay quality, and extracurricular engagement.
• Score: 1.9594639581421427
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Rethinking the Evaluation and Optimization of LLM-Based Social Simulation [56.18577997572273]
  We use the subjectivity coefficient to analyze how accuracy-based evaluation and hard-label training fail as subjectivity grows.<n>We construct SUBJSIM, a benchmark of 19,300 contexts covering 193 annotators and 100 subjective questions.<n>Results on SUBJSIM demonstrate the advantages of our method.
  arXiv  Detail & Related papers  (2026-08-20T06:26:03Z)
• A Controlled Synthetic Benchmark for Educational Aspect-Based Sentiment Analysis [47.027290803102666]
  This study introduces a controlled benchmark for educational ABSA built from 10,000 synthetic course reviews.<n>The strongest untuned model, BERT, reaches a held-out detection micro-F1 of 0.2760.<n>A conservative external evaluation on 2,829 mapped student-feedback reviews from Herath et al. yields a micro-F1 of 0.4593 for BERT on a 9-aspect overlap.
  arXiv  Detail & Related papers  (2026-05-25T07:05:21Z)
• PEEM: Prompt Engineering Evaluation Metrics for Interpretable Joint Evaluation of Prompts and Responses [5.396994583933599]
  We propose PEEM (Prompt Engineering Evaluation Metrics), a unified framework for evaluation of both prompts and responses.<n>PEEM defines a structured rubric with 9 axes: 3 prompt criteria (clarity/structure, linguistic quality, fairness) and 6 response criteria (accuracy, objectivity, relevance, clarity, conciseness)
  arXiv  Detail & Related papers  (2026-03-11T07:00:59Z)
• S-GRADES -- Studying Generalization of Student Response Assessments in Diverse Evaluative Settings [2.010691774041283]
  evaluating student responses from long essays to short factual answers is a key challenge in educational NLP.<n>We introduce S-GRADES, a web-based benchmark that consolidates 14 diverse grading datasets under a unified interface.<n>To demonstrate the utility of S-GRADES, we evaluate three state-of-the-art large language models across the benchmark using multiple reasoning strategies in prompting.
  arXiv  Detail & Related papers  (2026-03-10T21:11:51Z)
• OmniReview: A Large-scale Benchmark and LLM-enhanced Framework for Realistic Reviewer Recommendation [22.223973340236594]
  Profiling Scholars with Multi-gate Mixture-of-Experts (Pro-MMoE) is a novel framework that synergizes Large Language Models (LLMs) with Multi-task Learning.<n>Pro-MMoE achieves state-of-the-art performance across six of seven metrics, establishing a new benchmark for realistic reviewer recommendation.
  arXiv  Detail & Related papers  (2026-02-09T16:57:35Z)
• Encyclo-K: Evaluating LLMs with Dynamically Composed Knowledge Statements [78.87065404966002]
  Existing benchmarks predominantly curate questions at the question level.<n>We propose Encyclo-K, a statement-based benchmark that rethinks benchmark construction from the ground up.
  arXiv  Detail & Related papers  (2025-12-31T13:55:54Z)
• Evaluating the Ability of Large Language Models to Identify Adherence to CONSORT Reporting Guidelines in Randomized Controlled Trials: A Methodological Evaluation Study [7.142913983218931]
  This study aimed to evaluate the accuracy and reliability of contemporary LLMs in identifying adherence of published RCTs.<n>We constructed a golden standard dataset of 150 published RCTs spanning diverse medical specialties.<n>Top-performing models, Gemini-2.5-Flash and DeepSeek-R1, achieved nearly identical macro F1 scores of 0.634 and Cohen's Kappa coefficients of 0.280 and 0.282, respectively.
  arXiv  Detail & Related papers  (2025-11-17T08:05:15Z)
• NAIPv2: Debiased Pairwise Learning for Efficient Paper Quality Estimation [58.30936615525824]
  We present NAIPv2, a debiased and efficient framework for paper quality estimation.<n> NAIPv2 employs pairwise learning within domain-year groups to reduce inconsistencies in reviewer ratings.<n>It is trained on pairwise comparisons but enabling efficient pointwise prediction at deployment.
  arXiv  Detail & Related papers  (2025-09-29T17:59:23Z)
• CRACQ: A Multi-Dimensional Approach To Automated Document Assessment [0.0]
  CRACQ is a multi-dimensional evaluation framework tailored to evaluate documents across f i v e specific traits: Coherence, Rigor, Appropriateness, Completeness, and Quality.<n>It integrates linguistic, semantic, and structural signals into a cumulative assessment, enabling both holistic and trait-level analysis.
  arXiv  Detail & Related papers  (2025-09-26T17:01:54Z)
• LLMEval-3: A Large-Scale Longitudinal Study on Robust and Fair Evaluation of Large Language Models [51.55869466207234]
  Existing evaluation of Large Language Models (LLMs) on static benchmarks is vulnerable to data contamination and leaderboard overfitting.<n>We introduce LLMEval-3, a framework for dynamic evaluation of LLMs.<n>LLEval-3 is built on a proprietary bank of 220k graduate-level questions, from which it dynamically samples unseen test sets for each evaluation run.
  arXiv  Detail & Related papers  (2025-08-07T14:46:30Z)
• MetaQAP -- A Meta-Learning Approach for Quality-Aware Pretraining in Image Quality Assessment [2.578159662141357]
  Image Quality Assessment (IQA) is a critical task in a wide range of applications but remains challenging due to the subjective nature of human perception and the complexity of real-world image distortions.<n>This study proposes MetaQAP, a novel no-reference IQA model designed to address these challenges by leveraging quality-aware pre-training and meta-learning.<n>The proposed MetaQAP model achieved exceptional performance with Pearson Linear Correlation Coefficient (PLCC) and Spearman Rank Order Correlation Coefficient (SROCC) scores of 0.9885/0.9812 on LiveCD, 0.9702/0.9658 on Kon
  arXiv  Detail & Related papers  (2025-06-19T21:03:47Z)
• RAG-Zeval: Towards Robust and Interpretable Evaluation on RAG Responses through End-to-End Rule-Guided Reasoning [64.46921169261852]
  RAG-Zeval is a novel end-to-end framework that formulates faithfulness and correctness evaluation as a rule-guided reasoning task.<n>Our approach trains evaluators with reinforcement learning, facilitating compact models to generate comprehensive and sound assessments.<n>Experiments demonstrate RAG-Zeval's superior performance, achieving the strongest correlation with human judgments.
  arXiv  Detail & Related papers  (2025-05-28T14:55:33Z)
• T2I-Eval-R1: Reinforcement Learning-Driven Reasoning for Interpretable Text-to-Image Evaluation [60.620408007636016]
  We propose T2I-Eval-R1, a novel reinforcement learning framework that trains open-source MLLMs using only coarse-grained quality scores.<n>Our approach integrates Group Relative Policy Optimization into the instruction-tuning process, enabling models to generate both scalar scores and interpretable reasoning chains.
  arXiv  Detail & Related papers  (2025-05-23T13:44:59Z)
• J1: Incentivizing Thinking in LLM-as-a-Judge via Reinforcement Learning [54.85131761693927]
  We introduce J1, a reinforcement learning framework for teaching LLM judges to think before making decisions.<n>Our core contribution lies in converting all judgment tasks for non-verifiable and verifiable prompts into a unified format with verifiable rewards.<n>We then use RL to train thinking-judges at scales of 8B, 32B, and 70B and show that they obtain state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-05-15T14:05:15Z)
• HypoEval: Hypothesis-Guided Evaluation for Natural Language Generation [25.193026443079987]
  HypoEval is a Hypothesis-guided Evaluation framework for large language models (LLMs)<n>With only 30 human evaluations, HypoEval achieves state-of-the-art performance in alignment with both human rankings (Spearman correlation) and human scores (Pearson correlation)<n>We conduct systematic studies to assess the robustness of HypoEval, highlighting its effectiveness as a reliable and interpretable automated evaluation framework.
  arXiv  Detail & Related papers  (2025-04-09T18:00:01Z)
• Fairness Evaluation with Item Response Theory [10.871079276188649]
  This paper proposes a novel Fair-IRT framework to evaluate fairness in Machine Learning (ML) models. Detailed explanations for item characteristic curves (ICCs) are provided for particular individuals. Experiments demonstrate the effectiveness of this framework as a fairness evaluation tool.
  arXiv  Detail & Related papers  (2024-10-20T22:25:20Z)
• Learning to Love Edge Cases in Formative Math Assessment: Using the AMMORE Dataset and Chain-of-Thought Prompting to Improve Grading Accuracy [0.0]
  This paper introduces AMMORE, a new dataset of 53,000 math open-response question-answer pairs from Rori. We conduct two experiments to evaluate the use of large language models (LLM) for grading challenging student answers.
  arXiv  Detail & Related papers  (2024-09-26T14:51:40Z)
• Quantifying and Optimizing Global Faithfulness in Persona-driven Role-playing [37.92922713921964]
  Persona-driven role-playing (PRP) aims to build AI characters that can respond to user queries by faithfully sticking with all persona statements. This paper presents a pioneering exploration to quantify PRP faithfulness as a fine-grained and explainable criterion, which also serves as a reliable reference for optimization.
  arXiv  Detail & Related papers  (2024-05-13T13:21:35Z)
• Query Performance Prediction using Relevance Judgments Generated by Large Language Models [53.97064615557883]
  We propose a new Query performance prediction (QPP) framework using automatically generated relevance judgments (QPP-GenRE)<n>QPP-GenRE decomposes QPP into independent subtasks of predicting relevance of each item in a ranked list to a given query.<n>We predict an item's relevance by using open-source large language models (LLMs) to ensure scientific relevance.
  arXiv  Detail & Related papers  (2024-04-01T09:33:05Z)
• Evaluating and Improving Factuality in Multimodal Abstractive Summarization [91.46015013816083]
  We propose CLIPBERTScore to leverage the robustness and strong factuality detection performance between image-summary and document-summary. We show that this simple combination of two metrics in the zero-shot achieves higher correlations than existing factuality metrics for document summarization. Our analysis demonstrates the robustness and high correlation of CLIPBERTScore and its components on four factuality metric-evaluation benchmarks.
  arXiv  Detail & Related papers  (2022-11-04T16:50:40Z)
• Uncertainty-Driven Action Quality Assessment [11.958132175629368]
  We propose a novel probabilistic model, named Uncertainty-Driven AQA (UD-AQA), to capture the diversity among multiple judge scores.<n>We generate the estimation of uncertainty for each prediction, which is employed to re-weight AQA regression loss.<n>Our proposed method achieves competitive results on three benchmarks including the Olympic events MTL-AQA and FineDiving, and the surgical skill JIGSAWS datasets.
  arXiv  Detail & Related papers  (2022-07-29T07:21:15Z)
• Deep F-measure Maximization for End-to-End Speech Understanding [52.36496114728355]
  We propose a differentiable approximation to the F-measure and train the network with this objective using standard backpropagation. We perform experiments on two standard fairness datasets, Adult, Communities and Crime, and also on speech-to-intent detection on the ATIS dataset and speech-to-image concept classification on the Speech-COCO dataset. In all four of these tasks, F-measure results in improved micro-F1 scores, with absolute improvements of up to 8% absolute, as compared to models trained with the cross-entropy loss function.
  arXiv  Detail & Related papers  (2020-08-08T03:02:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.