Search Results: (16-30 of 71 records)
|REL 2021073||Using High School Data to Predict College Readiness and Early College Success on Guåhan (Guam)
On Guåhan (Guam), the large percentages of students enrolling in non-credit-bearing courses at Kulehon Kumunidåt Guåhan (Guam Community College) and Unibetsedåt Guåhan (University of Guam) have raised concerns about college readiness and early college success. Without adequate research on predictors of college readiness and early success among students on Guåhan, educators and other stakeholders find it difficult to identify and support students at risk of being underprepared for college. This study examined which student characteristics predicted college readiness and early college success among students who graduated from Guåhan high schools and enrolled at Kulehon Kumunidåt Guåhan or Unibetsedåt Guåhan between 2012 and 2015. Students' college readiness and early college success were assessed using three indicators: enrolling in only credit-bearing math and English courses during the first year of college, earning all credits attempted during the first semester of college, and persisting to a second year of college. About 23 percent of students met all three indicators and were thus classified as demonstrating college readiness and early college success. The percentages of students who met each individual indicator varied: 30 percent enrolled in only credit-bearing math and English courses, 43 percent earned all the credits they attempted, and 74 percent persisted to a second year. Various student characteristics predicted meeting all three indicators and each individual indicator. Graduates of John F. Kennedy High School and male students were the most likely to meet all three indicators and were the most likely to enroll in only credit-bearing math and English courses. Completing a high-level math course during high school positively predicted meeting the composite indicator of college readiness and early college success and of enrolling in only credit-bearing math and English courses and earning all credits attempted. A higher cumulative high school grade point average also positively predicted meeting all three indicators and each individual indicator. Kulehon Kumunidåt Guåhan enrollees were more likely than Unibetsedåt Guåhan enrollees to earn all credits attempted during their first semester.
|NFES 2021013|| Forum Guide to Strategies for Education Data Collection and Reporting (SEDCAR)
The Forum Guide to Strategies for Education Data Collection and Reporting (SEDCAR) was created to provide timely and useful best practices for education agencies that are interested in designing and implementing a strategy for data collection and reporting, focusing on these as key elements of the larger data process. It builds upon the Standards for Education Data Collection and Reporting (published by the Forum in 1991) and reflects the vast increase over the past three decades in the number of compulsory and/or continual data collections conducted by education agencies. This new resource is designed to be relevant to the state and local education agencies (SEAs and LEAs) of today, in which data are regularly collected for multiple purposes, and data collection and recording may be conducted by many different individuals within an agency.
|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 2019112||Education Demographic and Geographic Estimates Program (EDGE): School Neighborhood Poverty Estimates, 2016-2017
The National Center for Education Statistics (NCES) Education Demographic and Geographic Estimates (EDGE) program developed school neighborhood poverty estimates to provide an indicator of the economic conditions in neighborhoods where schools are located. These spatially interpolated demographic and economic (SIDE) estimates apply spatial statistical methods to existing sources of income and poverty data developed by the U.S. Census Bureau to produce new indicators with additional flexibility to support educational research. The economic conditions of neighborhoods around schools may or may not reflect the neighborhood conditions of students who attend the schools. However, supplemental information about school neighborhoods may be useful to combine with student-level or school-level information to provide a clearer picture of the overall educational environment.
|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.
|NCES 2018130||Education Demographic and Geographic Estimates (EDGE) Program: American Community Survey Comparable Wage Index for Teachers (ACS-CWIFT)
The Comparable Wage Index (CWI) is an index that was initially created by the National Center for Education Statistics (NCES) to facilitate comparison of educational expenditures across locales (principally school districts, or local educational agencies—LEAs) or states (state educational agencies— SEAs). The CWI is a measure of the systematic, regional variations in the wages and salaries of college graduates who are not PK-12 educators as determined by reported occupational category. It can be used by researchers to adjust district-level finance data at different levels in order to make better comparisons across geographic areas. This documentation describes the creation of a CWI for teachers based primarily on the American Community Survey (ACS). The ACS, an ongoing survey conducted by the U.S. Census Bureau, has replaced the decennial census as the primary source of detailed demographic information about the U.S. population. It provides information about the earnings, age, occupation, industry, and other demographic characteristics for millions of U.S. workers. The ACS-CWIFT measures wage and salary differences for college graduates, using an analysis that is modeled after the baseline analysis used to construct the original CWI released by NCES in 2006.
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