Ranking Resilience Attributes for Texas Public School Districts

Student learning, as measured by the State of Texas Assessment of Academic Readiness (STAAR) in public school systems in the US, plummeted during the COVID-19 pandemic, erasing years of improvements. In this body of research, we collect, integrate and analyze all available public data in the data science pipeline to see if public data can explain what factors contribute to learning loss recovery and inform public policy. This is a unique study of public data to address the post-COVID educational policy crisis from a data science perspective. To this end, we have developed an end-to-end large-scale educational data modeling pipeline that (i) integrates, cleans, and analyzes educational data; (ii) visualizes this data utilizing a free, opensource Python Panel dashboard; and (iii) implements automated attribute importance analysis to draw meaningful conclusions. We demonstrate a novel data-driven approach to discover insights from an extensive collection of disparate public data sources. We offer actionable insights to policymakers to identify the most affected areas to help policymakers’ direct resources to those areas and schools.

Discipline thesis:degree_discipline

Computer Science

Grantor

Texas State University

Year dc:date.issued

2023

Author dc:creator

• Payan, Daniel

Advisor dc:contributor.advisor

• Tesic, Jelena

Committee member dc:contributor.committeemember

• Feng, Li

• learning loss
• COVID-19
• learning recovery
• feature selection

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10877/17933

OAI identifier oai:identifier

oai:digital.library.txst.edu:10877/17933