We’re motivated by something greater than the bottom-line. We believe in supporting both the health of the education system in the United States and the well-being of each and every student. So we make a point to share what we learn with others who are positioned to make a difference, too.

Evidence-Based Practices for Assessing Students' Social and Emotional Well-Being

This brief is one in a series aimed at providing K-12 education decision makers and advocates with an evidence base to ground discussions about how to best serve students during and following the novel coronavirus pandemic.
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School Effects in Chain-Linked Predictive Analytics Models

In this memo, we investigate issues involved in adding school effects to chain-linked predictive analytics models and systems.
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An IRT Mixture Model for Rating Scale Confusion Associated with Negatively Worded Items in Measures of Social-Emotional Learning

We used mixture IRT models to evaluate confusion due to the negative wording of certain items on a social-emotional learning (SEL) survey. We also evaluated the consequences of the potential confusion. We found evidence of rating scale confusion due to negatively worded items. We also found that confusion was most prevalent at lower grade levels and was positively related to both reading proficiency and ELL status.
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Trends in Student Social-Emotional Learning: Evidence From the First Large-Scale Panel Student Survey

We used social-emotional learning survey data to simulate how four constructs—growth mindset, self-efficacy, self-management, and social awareness—develop from grades 4 to 12 and how these trends vary by gender, socioeconomic status, and race/ethnicity among students for two consecutive years. We found that, with the exception of growth mindset, self-reports of these constructs do not increase monotonically as students move through school; self-efficacy, social awareness, and, to a lesser degree, self-management decrease after Grade 6.
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Contingent and Predictive Analytics

This memo provides an introduction into contingent analytics, a framework that attempts to bridge two methodologies that are quite different, but related: (1) predictive analytics and early warning systems and (2) evaluation methods.
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Producing long-term forecasts of individual student outcomes: An application of chain-linked models with short-span data and education policy regime change

We focus in this paper on using predictive analytics models to produce forecasts of high school graduation and college persistence outcomes for students as early as 3rd grade.
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SEL Best Practices Guide

This guide outlines the steps that organizations might consider for measuring students’ social and emotional learning (SEL). We highlight the lessons we have learned from the research that Education Analytics has conducted on SEL survey measures. We also discuss future directions of SEL measurement that policymakers and practitioners at the state and district level should consider.
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Can We Measure Classroom Supports for Social-Emotional Learning?

We applied value-added models to student surveys in the CORE Districts to explore whether social-emotional learning (SEL) surveys can be used to measure effective classroom-level supports for SEL. We found that classrooms differ in their effect on students’ growth in self-reported SEL—even after accounting for school-level effects.
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Stability of School Contributions to Student Social-Emotional Learning Gains

We estimated and examined the stability of school effects on SEL across two years, using a large-scale SEL survey administered in California’s CORE districts. We found that correlations among school effects in the same grades across different years are positive, but they are lower than those for math and English Language Arts. Schools in the top or the bottom of the school effect distribution are more persistent in their impacts across years than those in the middle of the distribution.
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