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 Pub Number  Title  Date
REL 2022133 Branching Out: Using Decision Trees to Inform Education Decisions
Classification and Regression Tree (CART) analysis is a statistical modeling approach that uses quantitative data to predict future outcomes by generating decision trees. CART analysis can be useful for educators to inform their decisionmaking. For example, educators can use a decision tree from a CART analysis to identify students who are most likely to benefit from additional support early—in the months and years before problems fully materialize. This guide introduces CART analysis as an approach that allows data analysts to generate actionable analytic results that can inform educators’ decisions about the allocation of extra supports for students. Data analysts with intermediate statistical software programming experience can use the guide to learn how to conduct a CART analysis and support research directors in local and state education agencies and other educators in applying the results. Research directors can use the guide to learn how results of CART analyses can inform education decisions.
12/27/2021
REL 2021114 Using a survey of social and emotional learning and school climate to inform decisionmaking

The District of Columbia Public Schools (DCPS) has prioritized efforts to support students' social and emotional learning (SEL) competencies, such as perseverance and social awareness. To measure students' SEL competencies and the school experiences that promote SEL competencies (school climate), DCPS began administering annual surveys to students, teachers, and parents in 2017/18. DCPS partnered with the Mid-Atlantic Regional Educational Laboratory to study how the district could use these surveys to improve students' outcomes. The study found the following:

  • Students' SEL competencies and school experiences are the most favorable in elementary school and the least favorable in middle school and the beginning of high school. This pattern suggests that schools might provide targeted supports before or during grades 6-10 to promote SEL competencies and school experiences when students need the most support.
  • The trajectories of students' SEL competencies and school experiences differed in different schools, to a similar degree as trajectories in academic measures like test scores. To understand why changes in SEL competencies and school experiences differ across schools, DCPS could explore differences in practices between schools with better and worse trajectories. In addition, DCPS could provide targeted support to schools with lower levels of positive change.
  • Of the SEL competencies and school experiences in DCPS's survey, self-management—how well students control their emotions, thoughts, and behavior—is most related to students' later academic outcomes. Programs or interventions that target self-management might have the most potential for improving students' outcomes compared to those that target other SEL competencies or school experiences.
  • In statistical models designed to predict students' future academic outcomes, SEL competency and school experience data add little accuracy beyond prior academic outcomes (such as achievement test scores and attendance) and demographic characteristics. Prior academic outcomes and demographic characteristics predict later outcomes with a high degree of accuracy, and they may implicitly incorporate the SEL competencies and school experiences. These findings suggest that DCPS would not need to use SEL competencies and school experiences to identify whether or not students are at risk of poor academic outcomes.
  • Student, teacher, and parent reports on SEL competencies and school experiences are positively related across schools, but they also exhibit systematic differences, suggesting that some respondent groups may not be aligned in their view of SEL competencies and school experiences. These differences may serve as a tool to help DCPS target efforts to improve communication among students, teachers, and parents.
8/3/2021
NCEE 2020004 How States and Districts Support Evidence Use in School Improvement
The Every Student Succeeds Act encourages educators to use school improvement strategies backed by rigorous research. This snapshot, based on national surveys administered in 2018, describes what guidance states provided on improvement strategies and how districts selected such strategies in lowest-performing schools. Most states pointed districts and schools to evidence on improvement strategies, but few required schools to choose from a list of approved strategies. In turn, most districts reported that evidence of effectiveness was "very important" when choosing improvement strategies, but the evidence districts relied on probably varies in quality.
6/16/2020
NCES 2019084 Technology and K-12 Education: The NCES Ed Tech Equity Initiative
This interactive brochure provides an overview of the Initiative—including its purpose, goal, and target outcomes.
2/26/2019
NCES 2019085 Technology and K-12 Education: Advancing the NCES Ed Tech Equity Initiative
This infographic outlines the key steps NCES is taking to advance the NCES Ed Tech Equity Initiative.
2/26/2019
NCES 2019086 Technology and K-12 Education: The NCES Ed Tech Equity Initiative: Framework
Check out our new factsheet to learn about the factors most critical to informing ed tech equity in the context of K-12 education!
2/26/2019
NCES 2019087 Technology and K-12 Education: The NCES Ed Tech Equity Initiative: Data Collection Priorities
This factsheet outlines the key subtopics NCES will prioritize in its ed tech equity data collections.
2/26/2019
REL 2016130 Decision points and considerations for identifying rural districts that have closed student achievement gaps
Rural districts have long faced challenges in closing the achievement gap between high-poverty students and their more affluent peers. This research brief outlines key decision points and considerations for state and district decisionmakers who wish to identify rural districts that have closed academic achievement gaps. Examining these districts’ experiences with organizational and instructional policies and practices may suggest activities associated with making achievement gains and narrowing achievement gaps that can be systematically investigated. Key issues in the process are highlighted by examples from recent work with rural stakeholder groups in Colorado and Nebraska.
4/26/2016
NCSER 2015002 The Role of Effect Size in Conducting, Interpreting, and Summarizing Single-Case Research
The field of education is increasingly committed to adopting evidence-based practices. Although randomized experimental designs provide strong evidence of the causal effects of interventions, they are not always feasible. For example, depending upon the research question, it may be difficult for researchers to find the number of children necessary for such research designs (e.g., to answer questions about impacts for children with low-incidence disabilities). A type of experimental design that is well suited for such low-incidence populations is the single-case design (SCD). These designs involve observations of a single case (e.g., a child or a classroom) over time in the absence and presence of an experimenter-controlled treatment manipulation to determine whether the outcome is systematically related to the treatment.

Research using SCD is often omitted from reviews of whether evidence-based practices work because there has not been a common metric to gauge effects as there is in group design research. To address this issue, the National Center for Education Research (NCER) and National Center for Special Education Research (NCSER) commissioned a paper by leading experts in methodology and SCD. Authors William Shadish, Larry Hedges, Robert Horner, and Samuel Odom contend that the best way to ensure that SCD research is accessible and informs policy decisions is to use good standardized effect size measures—indices that put results on a scale with the same meaning across studies—for statistical analyses. Included in this paper are the authors' recommendations for how SCD researchers can calculate and report on standardized between-case effect sizes, the way in these effect sizes can be used for various audiences (including policymakers) to interpret findings, and how they can be used across studies to summarize the evidence base for education practices.
1/7/2016
REL 2015105 Professional learning communities facilitator's guide for the What Works Clearinghouse practice guide: Teaching academic content and literacy to English learners in elementary and middle school
The Professional Learning Communities Facilitator's Guide is designed to assist teams of educators in applying the evidence-based strategies presented in the Teaching Academic Content and Literacy to English Learners in Elementary and Middle School educator's practice guide, produced by the What Works Clearinghouse. Through this collaborative learning experience, educators will expand their knowledge base as they read, discuss, share, and apply key ideas and strategies to help K–8 English learners acquire the language and literacy skills needed to succeed academically.

The facilitator's guide employs a five-step cycle that encourages professional learning communities to debrief, define, explore, experiment, and reflect and plan. This cycle is supplemented with activities, handouts, readings, and videos. Participants will develop a working knowledge of some of the best practices in the English learner practice guide through analysis of teaching vignettes and other interactive activities. Included in the toolkit of materials are activities along with 31 handouts and 23 videos. Four of the videos provide a narrative overview of each of the four recommendations in the practice guide, and the remaining videos show actual classrooms from three different grade levels putting the recommendations into practice.
7/7/2015
NFES 2011802 Traveling Through Time: The Forum Guide to Longitudinal Data Systems Book IV: Advanced LDS Usage
This document, Book Four of Four: Advanced LDS Usage, is the fourth and final installment of this Forum series of guides on longitudinal data systems (LDS). One goal of the Forum is to improve the quality of education data gathered for use by policymakers and program decisionmakers. An approach to furthering this goal has been to pool the collective experiences of Forum members to produce “best practice” guides in areas of high interest to those who collect, maintain, and use data about elementary and secondary education. Developing LDSs is one of those high-interest areas. These systems hold promise for enhancing both the way education agencies use data to serve students and the way they do business, from the policy level to the school office and into the classroom.
7/25/2011
NFES 2011805 Traveling Through Time: The Forum Guide to Longitudinal Data Systems Book III: Effectively Managing LDS Data
This document, Book Three of Four: Effectively Managing LDS Data, is the third installment of this Forum series of guides on longitudinal data systems (LDS). One goal of the Forum is to improve the quality of education data gathered for use by policymakers and program decisionmakers. An approach to furthering this goal has been to pool the collective experiences of Forum members to produce “best practice” guides in areas of high interest to those who collect, maintain, and use data about elementary and secondary education. Developing LDSs is one of those high-interest areas. These systems hold promise for enhancing both the way education agencies use data to serve students and the way they do business, from the policy level to the school office and into the classroom.
2/7/2011
NFES 2011804 Traveling Through Time: The Forum Guide to Longitudinal Data Systems Book II: Planning and Developing an LDS
This book, Planning and Developing an LDS, is the second in a four-part series about longitudinal data systems (LDS). The first book, What is an LDS?, focused on the fundamental questions of what an LDS is (and what it is not), what steps should be taken to achieve a sound system, what components make up an ideal system, and why such a system is of value in education. The present installment discusses the early stages of LDS development, and will help state and local education agencies through the process of determining what they want to accomplish with their LDS and what they will need in order to achieve these goals. The organization’s vision for an LDS should be heavily informed by the needs of a broad range of stakeholders. Throughout the systems development life cycle, policymakers and system developers need to engage in self-assessment, identifying the system they have before figuring out what type of system they want. Policymakers’ requirements should be driven by the needs of the education community, the costs involved given the legacy system and staff, and the institutional support for the project. Planners should ensure project sustainability by creating interest and sustained buy-in, and by securing long-term funding. Procurement planning must be done, that is, lining up a vendor or building the staffing capacity to construct the system. In addition, having the right developers may not be enough: an informed commitment to building, using, and maintaining the LDS must permeate the organization to ensure long-term success. And, throughout the life of the system, thorough evaluation must be done on a regular basis to ensure continued data quality and user satisfaction.
1/19/2011
NFES 2010805 Traveling Through Time: The Forum Guide to Longitudinal Data Systems Book I: What is an LDS?
This first book in the guide series focuses on the fundamental questions of what an LDS is (and what it is not), what steps should be taken to achieve a sound system, what components make up an ideal system, and why such a system is of value in education. Chapter 1 introduces this guide series, discussing its purpose, format, and intended audience. Chapter 2 covers some LDS basics, defining the concept of a "longitudinal data system" and laying out key nontechnical steps to planning and developing a successful system. Chapter 3 presents the technical components that generally comprise an LDS, as well as some additional features that may enhance the system. Chapter 4 addresses some common misconceptions regarding longitudinal data systems. Chapter 5 discusses the overarching benefits of an LDS.
7/26/2010
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