This paper investigates the effect of student satisficing, the act of providing suboptimal survey responses, on data quality in a large-scale social-emotional learning survey. The study examines the prevalence and impact of satisficing among 409,721 students and finds that its effect on data quality is less significant than expected. The paper offers a practical approach to defining and calculating satisficing, useful for researchers, policymakers, and practitioners working with large-scale datasets.

This study examines the properties of California's CORE Districts' SEL survey, which measures social-emotional learning in students. The survey was given to over 400,000 students in grades 3-12. The study uses both classical test theory and item response theory frameworks to analyze the data and make recommendations for modeling and scaling SEL survey data. Policy implications are discussed for educators, administrators, policy makers, and other stakeholders.

The increase in students opting out of standardized tests is a threat to accurately measuring student achievement. This brief examines the effects of opting out in the CORE districts and models how it could affect accountability measures. More opt-outs could significantly impact some metrics, but the growth measure is largely unaffected. Metrics tracking achievement by cohort are at risk of bias, even with low levels of opting out. Adjusting for characteristics of students who take the tests could be a solution.

This paper examines which social-emotional skills are most important for student success and how they vary among student subgroups. It uses longitudinal data from over 200,000 fourth through seventh grade students in California to explore growth mindset gaps across student groups and shows the significant predictive power of growth mindset for academic achievement gains, even when controlling for other social-emotional skills and background characteristics.

This paper examines the use of social-emotional learning (SEL) measures to evaluate school-level growth in student outcomes. The study finds substantial differences across schools in SEL growth, suggesting that schools may contribute to students' SEL. However, caution is recommended in interpreting measures as the causal impacts of schools on SEL due to potential measurement error and omitted variables bias.

This paper uses a large dataset to confirm that self-management skills predict student success and are a better predictor of student learning than other socio-emotional skills. Students with higher levels of self-management experience almost 80 days of additional learning. The paper also describes self-management gaps across student groups.

This report explores the concept of "continuous improvement" in California's K-12 education system, defining it as a shared, evidence-based process that requires a change in culture and a substantial investment of time and resources. Interviews with education leaders reveal the need for capacity building and data use, as well as barriers including a lack of clarity and resources. Implementing continuous improvement system-wide requires a shift in mindset and culture, persistent effort, and a focus on student outcomes.

This brief examines the consistency of the drop in academic self-efficacy during middle school across various student groups, such as demographics, achievement level, and school of attendance. The study finds that the drop is essentially universal across all groups, despite some showing lower self-efficacy than others. Additionally, schools vary in their students' level of self-efficacy, but do not differ much in this trend. The drop in academic self-efficacy is steeper for female students, resulting in lower self-efficacy for girls than boys throughout middle and high school.

This study uses value-added models to explore whether social-emotional learning (SEL) surveys can measure effective classroom-level supports for SEL. Results show that classrooms differ in their effect on students' growth in self-reported SEL, suggesting that classroom-level effects may be larger than school-level effects. However, the low explanatory power of the SEL models means it's unclear if these are causal effects. The SEL measures also capture growth not measured by academic test scores, indicating that classrooms within schools may impact measurable student growth in SEL.

Effective data use is crucial for continuous improvement, but there is confusion about how it differs from data use for other purposes. This report explains what data are most useful for continuous improvement and presents a case study of how the CORE data collaborative uses a multiple-measures approach to support decision-making.