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A Hybrid Machine Learning Approach for Graduate Admission Prediction and Combined University-Program Recommendation

Melina Heidari Far, Elham Tabrizi

Abstract

Graduate admissions have become increasingly competitive. This study highlights the need for a hybrid machine learning framework for graduate admission prediction, focusing on high-quality similar applicants and a recommendation system. The dataset, collected and enriched by the authors, includes 13,000 self-reported GradCafe application records from 2021 to 2025, enriched with features from the OpenAlex API, QS World University Rankings by Subject, and Wikidata SPARQL queries. A hybrid model was developed by combining XGBoost with a residual refinement k-nearest neighbors module, achieving 87\% accuracy on the test set. A recommendation module, then built on the model for rejected applicants, provided targeted university and program alternatives, resulting in actionable guidance and improving expected acceptance probability by 70\%. The results indicate that university quality metrics strongly influence admission decisions in competitive applicant pools. The features used in the study include applicant quality metrics, university quality metrics, program-level metrics, and interaction features.

A Hybrid Machine Learning Approach for Graduate Admission Prediction and Combined University-Program Recommendation

Abstract

Graduate admissions have become increasingly competitive. This study highlights the need for a hybrid machine learning framework for graduate admission prediction, focusing on high-quality similar applicants and a recommendation system. The dataset, collected and enriched by the authors, includes 13,000 self-reported GradCafe application records from 2021 to 2025, enriched with features from the OpenAlex API, QS World University Rankings by Subject, and Wikidata SPARQL queries. A hybrid model was developed by combining XGBoost with a residual refinement k-nearest neighbors module, achieving 87\% accuracy on the test set. A recommendation module, then built on the model for rejected applicants, provided targeted university and program alternatives, resulting in actionable guidance and improving expected acceptance probability by 70\%. The results indicate that university quality metrics strongly influence admission decisions in competitive applicant pools. The features used in the study include applicant quality metrics, university quality metrics, program-level metrics, and interaction features.

Paper Structure

This paper contains 23 sections, 5 equations, 10 figures, 7 tables.

Table of Contents

  1. Introduction and Related Work
  2. Data and Preprocessing
  3. Dataset Collection
  4. University Data Enrichment
  5. Program Discipline Mapping
  6. Admission Outcome Processing
  7. Feature Selection and Preprocessing
  8. Feature Engineering Guided by Decision Tree Analysis
  9. Dataset Summary and Bias Considerations
  10. Methodology
  11. Problem Formulation and Experimental Setup
  12. Baseline Models and Selection Rationale
  13. Baseline Model Performance Comparison
  14. Test Set Performance and Feature Space Visualization
  15. Hybrid XGBoost + kNN Model
  16. ...and 8 more sections

Figures (10)

  • Figure 1: Number of self reported GradCafe application records by year.
  • Figure 2: Decision tree showing the main splits with numeric thresholds on the first two levels, simplified feature only nodes thereafter, and truncated leaves indicated by '(...)'.
  • Figure 3: Distribution of applicant GPAs in the final dataset. The histogram shows the number of applicants per GPA bin, and the overlaid line represents the smoothed density.
  • Figure 4: Distribution of applicants across academic disciplines
  • Figure 5: Graduate admission acceptance rate over time
  • ...and 5 more figures