Search Results: (1-15 of 703 records)
|NCES 2020035||First-Time Subbaccalaureate Students: An Overview of Their Institutions, Programs, Completion, and Labor Market Outcomes After 3 Years
This Statistics in Brief report examines the 3-year completion and employment outcomes of students who first entered postsecondary certificate and associate’s degree programs in 2011–12.
|NFES 2020083||Forum Guide to Data Governance
The Forum Guide to Data Governance highlights the multiple ways that data governance programs can benefit education agencies. It addresses the management, collection, use, and communication of education data; the development of effective and clearly defined data systems and policies to handle the complexity and necessary protection of data; and the continuous monitoring and decisionmaking needed in a regularly shifting data landscape. The Guide also features 12 case studies from state and local education agencies that have implemented effective data governance programs.
|NCES 2020122||2019-20 Common Core of Data (CCD) Preliminary Directory Files
On July 7th, 2020, the National Center for Education Statistics released the 2019-20 Common Core of Data (CCD) Preliminary Directory Files. These files are the product of the CCD data collection for the 2019-20 school year. Data are reported at district and school levels as of October 1, 2019. The preliminary directory files include basic identifying information for each public school and local education agency (LEA), including the NCES identification numbers, location and mailing address and some limited attributes about the school or LEA, such as type, operational status, the lowest and highest grade offered. The preliminary directories do not include aggregated demographic information such as student enrollment or teacher and staff counts. For the preliminary directory, NCES has only conducted a limited review of the files. It is meant to provide data users with a more timely release of basic information for school and LEA. The full CCD universe for SY 2019-20 (which will include demographic information) will be published in Spring 2021.
|REL 2020027||Using Data from Schools and Child Welfare Agencies to Predict Near-Term Academic Risks
This study provides information to administrators, research offices, and student support offices in local education agencies (LEAs) interested in identifying students who are likely to have near-term academic problems such as absenteeism, suspensions, poor grades, and low performance on state tests. It describes an approach for developing a predictive model and assesses how well the model identifies at-risk students using data from two LEAs in Allegheny County, Pennsylvania. It also examines which types of predictors—including those from school, social services, and justice system data systems—are individually related to each type of near-term academic problem to better understand the causes of why students might be flagged as at risk by the model and how best to support them. The study finds that predictive models which apply machine-learning algorithms to the data are able to identify at-risk students with a moderate to high level of accuracy. Data from schools are the strongest predictors across all outcomes, and predictive performance is not reduced much when excluding social services and justice system predictors and relying exclusively on school data. However, some out-of-school events are individually related to near-term academic problems, including child welfare involvement, emergency homeless services, and juvenile justice system involvement. The models are more accurate in a larger LEA than in a smaller charter network, and they are better at predicting low GPA, course failure, and below basic performance on state assessments in grades 3-8 than they are for chronic absenteeism, suspensions, and below basic performance on end-of-course high-school standardized assessments. Results suggest that many LEAs could apply machine-learning algorithms to existing school data to identify students who are at-risk of near-term academic problems that are known to be precursors to dropout.
|NCES 2020068||Process Data From the 2017 NAEP Grade 8 Mathematics Assessment
This report describes the contents of the first-ever NCES release of a response process dataset from the National Assessment of Educational Progress (NAEP). Response process data are the data generated from students’ interactions with a digitally based assessment. The data include the time students spend on assessment items; their keypresses as they progress through the assessment; how they use onscreen tools made available to all learners (such as the calculator); and the use of accommodations (for example, text-to-speech). The response process dataset files will be released as a restricted-use data (RUD) package including the response process data, as well as linked datasets on students’ responses to assessment items and their demographics and accommodation information. Data will be available only for students who were assessed using assessment items that were released to the public from the 2017 grade 8 mathematics assessment. People interested in accessing the data must obtain a restricted-use data license from NCES.
|NCES 2020167||STEM Occupational Intentions: Stability and Change Through High School
This Statistics in Brief provides information about the occupational expectations of high school freshmen in 2009 and how their expectations changed (or did not) by the spring of 2012. The focus is on expectations for a career in a STEM field, defined in this report as science, technology, engineering, and mathematics. The report draws on data from the High School Longitudinal Study of 2009 (HSLS:09).
|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.
|NCES 2020082||Male and female high school students’ expectations for working in a health-related field
This Data Point is based on data from the High School Longitudinal Study of 2009 (HSLS:09), a nationally representative, longitudinal study of more than 23,000 ninth-graders in 2009. Follow-up surveys were administered to the cohort in 2012 and 2013. It examines students’ expectations for a job in healthcare at age 30 when they were freshmen, and again in the spring of 2012. It provides a description of the percentage of students who expected to have a job in healthcare at age 30 in both 2009 and 2012, those who changed their expectations, and those who did not expect a job in healthcare at either time. It also describes differences between males and females in expectations for a job in healthcare.
|NCES 2020055||Students in Subbaccalaureate Health Sciences Programs: 2015–2016
This Data Point examines the enrollment and demographic characteristics of students enrolled in subbaccalaureate (certificate and associate’s degree) health sciences programs. The report uses data from the 2015–16 National Postsecondary Student Aid Study (NPSAS:16).
|NCES 2020034||Health and STEM Career Expectations and Science Literacy Achievement of U.S. 15-Year-Old Students
This report uses U.S. data from the Program for International Student Assessment (PISA), a nationally representative study of 15-year-old students. This brief details the percentage, and reports the average score, of students who foresee either a career in health fields or in science, technology, engineering, and math (STEM) fields. The report analyzes career expectations and science achievement by gender, race/ethnicity, immigration status, and a measure of socioeconomic status.
|IES 2020001REV||Cost Analysis: A Starter Kit
This starter kit is designed for grant applicants who are new to cost analysis. The kit will help applicants an a cost analysis, setting the foundation for more complex economic analyses.
|NCES 2020024||Projections of Education Statistics to 2028
Projections of Education Statistics to 2028 is the 47th in a series of publications initiated in 1964. This publication provides national-level data on enrollment, teachers, high school graduates, and expenditures at the elementary and secondary level, and enrollment and degrees at the postsecondary level for the past 15 years and projections to the year 2028. For the 50 states and the District of Columbia, the tables, figures, and text contain data on projections of public elementary and secondary enrollment and public high school graduates to the year 2028. The methodology section describes models and assumptions used to develop national- and state-level projections.
|NCES 2020027||Policies Outlining the Role of Sworn Law Enforcement Officers in Public Schools
The National Center for Education Statistics collects data on crime, violence, and safety in U.S. public schools through the School Survey on Crime and Safety (SSOCS). This Data Point report uses data from the 2017–18 SSOCS to examine the formal policies schools have in place to outline officers’ roles and responsibilities and whether these policies are aligned with the activities that sworn law enforcement officers participate in while at school.
|NCES 2020048||Teachers’ Use of Technology for School and Homework Assignments: 2018–19
This report provides statistics about the use of technology for homework assignments in grades 3–12. Data were provided by public school teachers about their homework practices and about their understanding of information technology available to their students outside of school.
|WWC 2020006||Intervention Report: Full Option Science System
This What Works Clearinghouse (WWC) intervention report summarizes the research on Full Option Science System (FOSS), a curriculum including content in the physical, earth, and life sciences that is designed to improve student science achievement in kindergarten through Grade 8. No eligible studies of FOSS met WWC design standards, so the WWC is unable to draw any conclusions at this time about the effectiveness of this program.
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