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.

该研究使用马尔可夫链模型作为创新方法进行入学人数预测，该方法在 Eastern Michigan University 的案例研究中得到了详细描述。尽管受到 COVID-19 等外部不确定性的挑战，马尔可夫链模型展现了令人印象深刻的准确性，预测结果与实际入学人数的平均差值不到 1%。研究最后讨论了未来的方向和机构间的合作机会。