Search Results: (1-15 of 43 records)
|REL 2021067||Early Childhood Data Use Assessment Tool
The Early Childhood Data Use Assessment Tool is designed to identify and improve data use skills among early childhood education (ECE) program staff so they can better use data to inform, plan, monitor, and make decisions for instruction and program improvement. Data use is critical in quality ECE programs but can be intimidating for some ECE program staff. This tool supports growth in their data use skills. The tool has three components: (1) a checklist to identify staff skills in using child assessment and administrative data, (2) a resource guide to identify professional development resources for improving data use skills, and (3) an action plan template to support planning for the development and achievement of data use goals. Results obtained from using the tool are meant by the developers to support instruction and program improvement through increased and structured use of data.
|REL 2021059||Using High School Data to Predict College Success in Palau
The purpose of this study was to examine the college success of students who graduated from Palau High School between spring 2013 and spring 2015 and who enrolled at Palau Community College the fall semester immediately following their high school graduation. It also examined the relationships between student characteristics and three college success outcomes. The study’s sample included 234 students. The college success outcomes used in the study were first-year college cumulative grade point average, persistence to a second year of college, and earning an associate degree or certificate. Using existing data, researchers calculated descriptive statistics to describe the percentage of students who met each college success outcome. Multiple logistic regression models determined which student demographic and academic preparation characteristics predicted meeting the college success outcomes. The study results show that 60 percent of all students had a first-year college cumulative grade point average of 2.0 or higher, 56 percent persisted to a second year, and 20 percent earned an associate degree or certificate within three years. Having higher high school grade point averages predicted having higher first-year college grade point averages and being more likely to complete a degree or certificate within three years. Additionally, having higher grade 12 Palau Achievement Test scores, a standardized test administered in the Republic of Palau, predicted being more likely to have a higher first-year college grade point average. High school English course grades also predicted some college success outcomes. Specifically, earning a grade C or higher in grade 9 English predicted completing a certificate or degree within three years, and earning a grade of C or higher in grade 12 English predicted persisting to a second year. Finally, students who enrolled in the Palau High School Construction Technology Career Academy were less likely than students in other career academies to persist to a second year or complete a degree or certificate within three years. These findings suggest that most students achieved the early college success outcomes of earning a first-year college grade point average of 2.0 or higher and persisting to a second year, but that this did not always translate to graduating with a college degree or certificate within three years. Providing additional supports for students in college based on their high school performance, examining supports available for English learners at Palau High school, and reviewing the alignment of the Palau High School Construction Technology Career Academy and the needs of students who plan to attend college could help inform efforts to support the college success of students in Palau. Palau Community College may also want to conduct future studies to examine additional factors at the college, such as the effects of course sequencing or academic counseling services, to improve college success.
|NFES 2021023||School Courses for the Exchange of Data (SCED) Uses and Benefits
The School Courses for the Exchange of Data (SCED) Uses and Benefits publication was developed to provide a brief overview of SCED, highlight the research application and benefits of SCED to users, and illustrate SCED uses with case studies. SCED is a voluntary, common classification system for prior-to-secondary and secondary school courses. It can be used to compare course information, maintain longitudinal data about student coursework, and efficiently exchange coursetaking records. SCED is a free resource intended for federal, state, and local education agencies.
|REL 2021054||How Nebraska Teachers Use and Perceive Summative, Interim, and Formative Data
Teachers have access to more data than ever before, including summative (state-level), interim (benchmark-level), and formative (classroom-level) assessment data. Yet research on how often and why teachers use each type of these data is scarce. The Nebraska Department of Education partnered with the Regional Educational Laboratory Central to conduct a study of teachers and principals in 353 Nebraska schools to learn about teachers’ use and perceptions of summative, interim, and formative data and inform a state-level professional learning plan to support teachers’ data use. The findings indicate that teachers used formative data more often than interim or summative data and they perceived formative data to be more useful. Teachers with the least experience (5 years or less) reported using formative data more often than did teachers with the most experience (22 years or more). Teachers' perceptions of their competence in using data, their attitudes toward data, and their perceptions of organizational supports for data use (professional learning, principal leadership, and computer systems) were each positively associated with teachers' instructional actions with data. When teachers reported greater competence in using data, more positive attitudes toward data, or more organizational supports for data use, they more often took instructional actions with formative and interim data. Teachers with an advanced degree reported that they felt more competent in, and positive toward, using data than did teachers with a bachelor's degree.
|REL 2021052||An Approach to Using Student and Teacher Data to Understand and Predict Teacher Shortages
Addressing teacher shortages has been a persistent concern among leaders in schools, districts, state education agencies, and the federal government. This report describes an approach to identifying patterns of teacher shortages that was collaboratively developed by the Missouri Department of Elementary and Secondary Education and the Regional Educational Laboratory Central. The approach is implemented using widely available software. It can be adopted or adapted by education agencies that wish to understand and predict teacher shortages, including shortage trends in content and certification areas, in their own contexts. Education agencies may also use teacher shortage predictions to inform efforts to address inequities in students’ access to excellent educators.
|NCES 2021176||2012 Beginning Postsecondary Students Longitudinal Study (BPS:12) Postsecondary Education Transcript Study (PETS): Data File Documentation
This publication describes the methodology used in the 2012/17 Beginning Postsecondary Students Longitudinal Study Postsecondary Education Transcript Study. BPS:12 PETS is the third data release for a study of a nationally representative sample of first-time beginning postsecondary students who were surveyed 3 times over 6 academic years, in 2011-12, 2014, and 2017. Postsecondary academic transcripts were requested from all institutions attended by sample members. These transcript data include detailed information, by institution attended and by time periods, on enrollment, degree programs, fields of study, course taking, credit accumulation, and academic performance.
|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.
|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 2020441||2016/17 Baccalaureate and Beyond Longitudinal Study (B&B:16/17)
This publication describes the methods and procedures used in the 2016/17 Baccalaureate and Beyond Longitudinal Study (B&B:16/17). These graduates, who completed the requirements for a bachelor’s degree during the 2015–16 academic year, were first interviewed as part of the 2016 National Postsecondary Student Aid Study (NPSAS:16), and then followed up one year later in 2017. B&B:16/17 is the first follow-up interview of this cohort. This report details the methodology and outcomes of the B&B:16/17 student interview data collection and administrative records matching.
|NCES 2020522||Beginning Postsecondary Students Study 12/17 (BPS:12/17): Data File Documentation
This publication describes the methodology used in the 2012/17 Beginning Postsecondary Students Longitudinal Study (BPS:12/17). BPS:12/17 is the second and final follow-up study of students who began postsecondary education in the 2011 – 12 academic year. These students were first interviewed as part of the 2011 – 12 National Postsecondary Student Aid Study (NPSAS:12). In particular, this report details the methodology and outcomes of the BPS:12/17 sample design, student interview design, student interview data collection processes, administrative records matching, data file processing, and weighting procedures. The BPS study is unique in that it includes both traditional and nontraditional students, follows their paths through postsecondary education over the course of 6 years, and is not limited to enrollment at a single institution.
|NFES 2019160||Forum Guide to Personalized Learning Data
The Forum Guide to Personalized Learning Data is designed to assist education agencies as they consider whether and how to use personalized learning. It provides an overview of personalized learning and describes best practices used by education agencies to collect data for personalized learning; to use those data to meet goals; and to support relationships, resources, and systems needed for the effective use of data in personalized learning. Personalized learning is still a developing prospect in many locations. therefore, the concepts and examples provided are intended to help facilitate idea sharing and discussion.
|NFES 2017168||Forum Guide to Reporting Civil Rights Data
The Forum Guide to Reporting Civil Rights Data presents a variety of effective methods through which local education agencies (LEAs) report civil rights data to the U.S. Department of Education’s Office for Civil Rights. In addition, the guide provides examples of how state education agencies can voluntarily help their LEAs with Civil Rights Data Collection (CRDC) reporting. The guide includes an overview of the CRDC, a discussion of the challenges and opportunities in reporting civil rights data, an explanation of the CRDC reporting process, and case studies that examine how specific education agencies report civil rights data.
|NCES 2017095||Technical Report and User Guide for the 2015 Program for International Student Assessment (PISA)
This technical report and user guide is designed to provide researchers with an overview of the design and implementation of PISA 2015 in the United States, as well as information on how to access the PISA 2015 data. The report includes information about sampling requirements and sampling in the United States; participation rates at the school and student level; how schools and students were recruited; instrument development; field operations used for collecting data; detail concerning various aspects of data management, including data processing, scaling, and weighting. In addition, the report describes the data available from both international and U.S. sources, special issues in analyzing the PISA 2015 data, as well as a description of merging data files.
|NCES 2018411||1996/2001 Beginning Postsecondary Students Longitudinal Study Restricted-Use Data File (including the 2015 Federal Student Aid Supplement)
The 1996/01 Beginning Postsecondary Students Longitudinal Study (BPS:96/01) restricted-use data file contains data on a nationally representative sample of students who began postsecondary education for the first time in the 1995-96 academic year. These sample members were interviewed in their first, third, and sixth year since entering college. These record-level data are based on student interviews and other administrative data sources and allow users to examine topics related to enrollment, persistence, and degree attainment over six academic years, from 1995-96 to 2000-01. The file includes data from 2015 Federal Student Aid Supplement, which appended student loan data from the National Student Loan Data System through 2015.
|NCES 2018412||2004/2009 Beginning Postsecondary Students Longitudinal Study Restricted-Use Data File (including the 2015 Federal Student Aid Supplement and postsecondary education transcripts)
The 2004/09 Beginning Postsecondary Students Longitudinal Study (BPS:04/09) restricted-use data file contains data on a nationally representative sample of students who began postsecondary education for the first time in the 2003-04 academic year. These sample members were interviewed in their first, third, and sixth year since entering college. These record-level data are based on student interviews and other administrative data sources and allow users to examine topics related to enrollment, persistence, and degree attainment over six academic years, from 2003-04 to 2008-09. The file includes data from the postsecondary education transcripts (PETS) and the 2015 Federal Student Aid Supplement (which appended student loan data from the National Student Loan Data System through 2015).
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