AASA What Works: Data Analytics to Assess Learning Loss and Recovery
Join AASA’s guest, data analytics expert Dr. John Gatta in examining how to quantify learning loss and develop personalized learning recovery plans
Leveraging predictive models to measure learning loss and growth recovery during COVID-19 school closures
Measuring learning loss requires that we recognize that student growth is personal in the sense that every individual student’s learning trajectory leading up to the pandemic was different.
Measuring and Recovering Learning Losses During the COVID-19 Pandemic
Documenting learning loss is important as the effects of school closures on learning loss are likely differential and asymmetric, resulting in large losses for some students, and negligible or negative losses for other students.
Case Study: Prioritizing Social & Emotional Learning at Barrington CUSD 220
Barrington District 220 did not set about their SEL journey looking for a quick win, but rather to foster a positive climate and culture.
Case Study: Using Data to Improve Instruction at PBL CUSD 10
PBL CUSD 10 sought a platform that would allow them to compare their student data against both national, state, and local norms.
Case Study: Leading an Evidence-Based Culture at KCSD96
Through a process of careful data analysis and community feedback, the KCSD96 Board of Education is constantly evaluating how to better serve the community.
Effect Sizes as Evidence – The single most important pitfall to avoid
Despite widespread use of effect sizes across industries as a standardized measure of impact, effect size calculations remain one of the most incorrectly applied and misinterpreted statistics. An effect size is nothing more than a standardized comparison, or “effect” , that captures the difference between an average value and a meaningful comparison in the metric of standard deviation.
The single most important pitfall for Illinois schools to avoid when interpreting ISBE Student Growth Percentiles (SGPs)
The Illinois State Board of Education (ISBE) recently released student growth percentiles (SGPs) to Illinois school districts in preparation of the new school accountability system and the launch of the new Illinois school report card. While incorporating student growth into the school accountability system is a step in the right direction, it is important to recognize that ISBE SGP results are reported within an accountability context not a school improvement context.
ISBE Student Growth Percentile Model Under ESSA
Based on recent information provided by the Accountability Technical Advisory Committee’s (TAC) recommendations to ISBE, it is likely that ISBE will move away from linear regression toward Student Growth Percentiles (SGPs) as a means to measure student growth under the new school improvement and accountability system.
Linear Regression and the ISBE Growth Model
I’m getting a lot of questions related to linear regression and how the Illinois State Board of Education (ISBE) may implement linear regression to measure student growth under the new school improvement and accountability system. As a result, I decided to synthesize information as it becomes available.
