From Data to Action: Predicting School Accountability Outcomes to Guide Targeted Interventions

• In Event: AERA Roundtable Session Sunday 9:45 am - Gold 3
  In Roundtable Session: Reimagining School Improvement: Research, Policy, and Practice in a Changing Educational Landscape

Abstract

This quantitative, non-experimental predictive study examined how 8 campus-level variables—teacher experience, principal experience, attendance rate, student mobility, economically disadvantaged status, math achievement, reading achievement, and locale—predict whether a Texas elementary or middle school receives a Comprehensive Support and Improvement (CSI) designation. Guided by systems theory, the study used logistic regression analysis on Texas Academic Performance Reports (TAPR) data from 2019, 2022, and 2023 to assess each variable’s individual and collective impact. Findings aim to improve early identification of at-risk campuses, inform data-driven decision-making, and support targeted interventions by offering a predictive model that strengthens school accountability and equity-focused policy development. Insights are expected to help school leaders address challenges and allocate resources more strategically to improve student outcomes.

Authors

• Jose Garza, Edinburg CISD

• Jeffery Chernosky, Texas A&M University - Kingsville

• Kelly S. Hall, Texas A&M University - Kingsville

• Robert Ayala, Texas A&M University - Kingsville

• Jingbo Liu, Texas A&M University - Kingsville

• Linda Villarreal, Texas A&M University - Kingsville