The Typology of Public Schools in the State of Louisiana and Interventions to Improve Performance: A Machine Learning Approach

Abstract

Extant literature on education research focuses on evaluating schools’ academic performance rather than the performance of educational institutions. Moreover, the State of Louisiana public school system always performs poorly in education outcomes compared to other school systems in the U.S. One of the limiting factors is the stringent standards applied among heterogeneous schools, steaming from the fit-for-all policies. We use a pairwise controlled manifold approximation technique and gradient boosting machine algorithm to typify Louisiana public schools into homogenous clusters and then characterize each identified group. The analyses uncover critical features of failing and high-performing school systems. Results confirm the heterogeneity of the school system, and each school needs tailored support to buoy its performance. Short-term interventions should focus on customized administrative and academic protocols with malleable interpositions addressing individual school shortcomings such as truancy. Long-term policies must discourse authentic economic development programs to foster community engagement and creativity while allocating strategic resources that cultivate resilience at the school and community levels.

Keywords

• clustering
• education
• machine learning
• multiclass
• PaCMAP
• typology