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 Pub Number  Title  Date
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.
7/6/2020
NCES 2020047 U.S. PIAAC Skills Map: State and County Indicators of Adult Literacy and Numeracy
The U.S. PIAAC Skills Map allows users to access estimates of adult literacy and numeracy proficiency in all U.S. states and counties through heat maps and summary card displays. It also provides estimates of the precision of its indicators and facilitates statistical comparisons among states and counties.
4/14/2020
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.
12/5/2019
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.
11/8/2019
NCEE 20194008 Evaluation of Support for Using Student Data to Inform Teachers' Instruction
Most districts help teachers use data to improve student learning, often supporting this effort with federal funds. But many teachers feel unprepared to use student data to inform their instruction — referred to as data-driven instruction (DDI) — and there is little evidence of whether it improves student achievement. This report assesses an intensive approach to supporting teachers' use of student data to tailor their instruction. The report found that this specific approach to DDI did not improve students' achievement, perhaps because the approach did not change teachers' reported use of data or classroom practices.
9/30/2019
REL 2017269 Comparing enrollment, characteristics, and academic outcomes of students in developmental courses and those in credit-bearing courses at Northern Marianas College
This study reports on the academic outcomes of full-time first time freshman seeking associate degrees who entered Northern Marianas College from fall semester 2008 through fall semester 2010. In English, 80.1 percent of these students enrolled in developmental courses; in math, 91 percent enrolled in developmental courses. To determine their academic outcomes, these students were tracked for eight semesters after their first year in college. The study found that students who initially enrolled in credit-bearing English or math classes had consistently more positive outcomes than students who initially enrolled in non-credit developmental English or math courses.
4/26/2017
REL 2017268 Using high school data to understand college readiness in the Northern Mariana Islands
This report examines the college readiness of public high school graduates in the Northern Mariana Islands as measured by whether the graduates were placed in developmental college courses or credit bearing college courses at Northern Marianas College. The study examined the high school records of recent graduates of the public school system in the Northern Mariana Islands who entered Northern Marianas College from fall semester 2010 through spring semester 2014. Demographic information was available about students' gender, ethnicity, primary language spoken at home, and economic need (based on whether or not students received Pell grants). The study found that 19.6 percent of students placed into credit-bearing English courses. Nearly 23 percent of female students, compared to about 16 percent of male students, placed into credit-bearing English courses. In math, 7.8 percent of students placed into credit-bearing courses. Students who did not receive Pell grants were more likely to place into credit-bearing math courses.
4/26/2017
REL 2017240 School discipline data indicators: A guide for districts and schools
Disproportionate rates of suspension for students of color are a local, state, and national concern. In particular, African American, Hispanic/Latino(a), and American Indian students experience suspensions more frequently than their White peers. Disciplinary actions that remove students from classroom instruction undermine their academic achievement and weaken their connection with school. This REL Northwest guide is designed to help educators use data to reduce disproportionate rates of suspension and expulsion based on race or ethnicity. It provides examples of selecting and analyzing data to determine whether racial disproportionality exists in a school or district's discipline practices. The guide also describes how to apply the Plan-Do-Study-Act continuous improvement cycle to inform intervention decisions and monitor progress toward desired outcomes.
4/14/2017
REL 2017263 Analyzing student-level disciplinary data: A guide for districts
The purpose of this report is to help guide districts in analyzing their own student-level disciplinary data to answer important questions about the use of disciplinary actions. This report, developed in collaboration with the Regional Educational Laboratory Northeast and Islands Urban School Improvement Alliance, provides information to district personnel about how to analyze their student-level data and answer questions about the use of disciplinary actions, such as whether these actions are disproportionately applied to some student subgroups, and whether there are differences in student academic outcomes across the types of disciplinary actions that students receive. This report identifies several considerations that should be accounted for prior to conducting any analysis of student-level disciplinary data. These include defining all data elements to be used in the analysis, establishing rules for transparency (including handling missing data), and defining the unit-of-analysis. The report also covers examples of descriptive analyses that can be conducted by districts to answer questions about their use of the disciplinary actions. SPSS syntax is provided to assist districts in conducting all of the analyses described in the report. The report will help guide districts to design and carry out their own analyses, or to engage in conversations with external researchers who are studying disciplinary data in their districts.
3/29/2017
REL 2017221 The "I" in QRIS Survey: Collecting data on quality improvement activities for early childhood education programs
Working closely with the Early Childhood Education Research Alliance and Iowa’s Quality Rating System Oversight Committee, Regional Educational Laboratory Midwest developed a new tool—the "I" in QRIS Survey—to help states collect data on the improvement activities and strategies used by early childhood education (ECE) providers participating in a Quality Rating and Improvement System (QRIS). As national attention increasingly has focused on the potential for high-quality early childhood education and care to reduce school-readiness gaps, states developed QRIS to document the quality of ECE programs, support systematic quality improvement efforts, and provide clear information to families about their child care choices. An essential element of a QRIS is the support offered to ECE providers to assist them in improving their quality. Although all the Midwestern states offer support to ECE providers to improve quality as part of their QRIS, states do not collect information systematically about how programs use these quality improvement resources. This survey measures program-level participation in workshops and trainings, coaching, mentoring, activities aimed at increasing the educational attainment of ECE staff, and financial incentive to encourage providers to improve quality. States can use this tool to document the current landscape of improvement activities, to identify gaps or strengths in quality improvement services offered across the state, and to identify promising improvement strategies. The survey is intended for use by state education agencies and researchers interested in the "I" in QRIS and can be adapted for their specific state context.
2/28/2017
REL 2017167 A comparison of two approaches to identifying beating-the-odds high schools in Puerto Rico
The Regional Educational Laboratory Northeast and Islands conducted this study using data on public high schools in Puerto Rico from national and territory databases to compare methods for identifying beating-the-odds schools. Schools were identified by two methods, a status method that ranked high-poverty schools based on their current observed performance and an exceeding-achievement-expectations method that ranked high-poverty schools based on the extent to which their actual performance exceeded (or fell short of) their expected performance. Graduation rates, reading proficiency rates, and mathematics proficiency rates were analyzed to identify schools for each method. The identified schools were then compared by method to determine agreement rates—that is, the amount of overlap in schools identified by each method. The report presents comparisons of the groups of schools—those identified by each method and all public high-poverty high schools in Puerto Rico—on descriptive information. Using the two methods—ranking by status and ranking by exceeding-achievement-expectations—two different lists of beating-the-odds schools were identified. The status method identified 17 schools, and the exceeding-achievement-expectations method identified 15 schools. Six schools were identified by both methods. The agreement rate between the two lists of beating-the-odds schools was 38 percent. The analyses suggest that using both methods to identify beating-the-odds schools is the best strategy because high schools identified by both methods demonstrate high levels of absolute performance and appear to be achieving higher levels of graduation rates and percent proficiency than might be expected given their demographics and prior performance.
12/6/2016
NFES 2017016 Forum Guide to Data Visualization: A Resource for Education Agencies
The purpose of this publication is to recommend data visualization practices that will help education agencies communicate data meaning in visual formats that are accessible, accurate, and actionable for a wide range of education stakeholders. Although this resource is designed for staff in education agencies, many of the visualization principles apply to other fields as well.
10/31/2016
REL 2016218 Self-study guide for implementing high school academic interventions
This Self-study Guide for Implementing High School Academic Interventions was developed to help district- and school-based practitioners plan and implement high school academic interventions. It is intended to promote reflection about current district and school strengths and challenges in planning for implementation of high school academic interventions, spark conversations among staff, and identify areas for improvement. The guide provides a template for data collection and guiding questions for discussion that may improve the implementation of high school academic interventions and decrease the number of students failing to graduate from high school on time.
8/23/2016
REL 2016164 Survey methods for educators: Analysis and reporting of survey data (part 3 of 3)
Educators at the state and local levels are increasingly using data to inform policy decisions. While student achievement data is often used to inform instructional or programmatic decisions, educators may also need additional sources of data, some of which may not be housed in their existing data systems. Creating and administering surveys is one way to collect such data. However, documentation available to educators about administering surveys may provide insufficient guidance about sampling or analysis approaches. Furthermore, some educators may not have training or experience in survey methods. In response to this need, REL Northeast & Islands created a series of three complementary guides that provide an overview of the survey research process designed for educators. The guides describe (1) survey development, (2) sampling respondents and survey administration, and (3) analysis and reporting of survey data.

Part three of this series, "Analysis and Reporting of Survey Data," outlines the following steps, drawn from the research literature:

1. Review the analysis plan
2. Prepare and check data files
3. Calculate response rates
4. Calculate summary statistics
5. Present the results in tables or figures
The guide provides detailed, real-world examples of how these steps have been used in a REL research alliance project. With this guide, educators will be able to analyze and report their own survey data.
8/2/2016
REL 2016160 Survey methods for educators: Selecting samples and administering surveys (part 2 of 3)
Educators at the state and local levels are increasingly using data to inform policy decisions. While student achievement data is often used to inform instructional or programmatic decisions, educators may also need additional sources of data, some of which may not be housed in their existing data systems. Creating and administering surveys is one way to collect such data. However, documentation available to educators about administering surveys may provide insufficient guidance about sampling or analysis approaches. Furthermore, some educators may not have training or experience in survey methods. In response to this need, REL Northeast & Islands created a series of three complementary guides that provide an overview of the survey research process designed for educators. The guides describe (1) survey development, (2) sampling respondents and survey administration, and (3) analysis and reporting of survey data.

Part two of this series, "Sampling Respondents and Survey Administration," outlines the following steps, drawn from the research literature:

1. Define the population
2. Specify the sampling procedure
3. Determine the sample size
4. Select the sample
5. Administer the survey
The guide provides detailed, real-world examples of how these steps have been used in a REL research alliance project. With this guide, educators will be able to develop their own sampling and survey administration plans.
8/2/2016
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