# A Practice in Enrollment Prediction with Markov Chain Models

arXiv: https://arxiv.org/abs/2405.14007 · HTML: https://mlsanity.com/paper/2405.14007/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 7 references that are in the map.
- First posted on arXiv 22 May 2024.

## Built on by (most cited first, 1 of 1)

- [AI-Driven Strategies for Reducing Student Withdrawal -- A Study of EMU Student Stopout](https://mlsanity.com/paper/2408.02598/) (2024)

## Abstract (arXiv)

Enrollment projection is a critical aspect of university management, guiding decisions related to resource allocation and revenue forecasting. However, despite its importance, there remains a lack of transparency regarding the methodologies utilized by many institutions. This paper presents an innovative approach to enrollment projection using Markov Chain modeling, drawing upon a case study conducted at Eastern Michigan University (EMU). Markov Chain modeling emerges as a promising approach for enrollment projection, offering precise predictions based on historical trends. This paper outlines the implementation of Enhanced Markov Chain modeling at EMU, detailing the methodology used to compute transition probabilities and evaluate model performance. Despite challenges posed by external uncertainties such as the COVID-19 pandemic, Markov Chain modeling has demonstrated impressive accuracy, with an average difference of less than 1 percent between predicted and actual enrollments. The paper concludes with a discussion of future directions and opportunities for collaboration among institutions.