A Practice in Enrollment Prediction with Markov Chain Models

TL;DR

This paper demonstrates how Markov Chain models can accurately predict university enrollment trends, providing a transparent and effective tool for institutional planning despite external uncertainties like COVID-19.

Contribution

It introduces an Enhanced Markov Chain approach for enrollment prediction and details its implementation and performance at Eastern Michigan University.

Findings

Prediction accuracy within 1% of actual enrollments

Effective handling of external uncertainties like COVID-19

Provides a transparent methodology for enrollment projection

Abstract

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.