Date of Award

6-2025

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Data Science

First Advisor

Dr. Mahbubul Majumder

Abstract

Teacher attrition is a growing concern in Nebraska, particularly in rural and high-need districts where staffing challenges are most acute. This study applies a hybrid approach combining Markov Chain modeling and Graph Theory to analyze over three decades of longitudinal employment data from the Nebraska Department of Education. A discrete-time Markov model is used to estimate transition probabilities between roles such as Teacher, Administrator, and Exit, revealing high attrition risks among early-career educators and paraprofessionals. To complement this, a directed weighted graph is constructed to examine the structural properties of role transitions using centrality, clustering, and entropy metrics. Results show that the educator workforce network is highly connected but structurally shallow, with limited upward mobility and a strong directional flow toward exit. By integrating probabilistic and structural analysis, this research offers actionable insights for retention policy and career pathway development aimed at building a more stable and equitable education system in Nebraska.

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