A Practice in Enrollment Prediction with Markov Chain Models
This addresses the problem of opaque enrollment projection methods for university administrators, though it is incremental as it builds on existing Markov Chain approaches.
The paper tackled enrollment prediction for university management by applying an Enhanced Markov Chain model to Eastern Michigan University data, achieving an average prediction error of less than 1% compared to actual enrollments.
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.