Search Results: (1-15 of 67 records)
|Student Reports of Bullying: Results From the 2022 School Crime Supplement to the National Crime Victimization Survey
The tables in this report present data on bullying in grades 6–12 from the 2022 School Crime Supplement (SCS) to the National Crime Victimization Survey (NCVS). The tables show how bullying victimization varies by student and school characteristics such as sex, race/ethnicity, grade, household income, region, urbanicity, enrollment size, and school poverty. The tables also show how rates of bullying victimization vary by crime-related variables such as the presence of gangs, guns, drugs, alcohol, and hate-related graffiti at school; selected school security measures; student criminal victimization; personal fear of attack or harm; avoidance behaviors; fighting; and the carrying of weapons.
|User’s Manual for the MGLS:2017 Data File, Restricted-Use Version
This manual provides guidance and documentation for users of the Middle Grades Longitudinal Study of 2017–18 (MGLS:2017) restricted-use school and student data files (NCES 2023-131). An overview of MGLS:2017 is followed by chapters on the study data collection instruments and methods; direct and indirect student assessment data; sample design and weights; response rates; data preparation; data file content, including the composite variables; and the structure of the data file. Appendices include a psychometric report, a guide to scales, field test reports, and school and student file variable listings.
|Overview of the Middle Grades Longitudinal Study of 2017–18 (MGLS:2017): Technical Report
This technical report provides general information about the study and the data files and technical documentation that are available. Information was collected from students, their parents or guardians, their teachers, and their school administrators. The data collection included direct and indirect assessments of middle grades students’ mathematics, reading, and executive function, as well as indirect assessments of socioemotional development in 2018 and again in 2020. MGLS:2017 field staff provided additional information about the school environment through an observational checklist.
|Identifying Students At Risk Using Prior Performance Versus a Machine Learning Algorithm
This report provides information for administrators in local education agencies who are considering early warning systems to identify at-risk students. Districts use early warning systems to target resources to the most at-risk students and intervene before students drop out. Schools want to ensure the early warning system accurately identifies the students that need support to make the best use of available resources. The report compares the accuracy of using simple flags based on prior academic problems in school (prior performance early warning system) to an algorithm using a range of in- and out-of-school data to estimate the specific risk of each academic problem for each student in each quarter. Schools can use one or more risk-score cutoffs from the algorithm to create low- and high-risk groups. This study compares a prior performance early warning system to two risk-score cutoff options: a cutoff that identifies the same percentage of students as the prior performance early warning system, and a cutoff that identifies the 10 percent of students most at risk.
The study finds that the prior performance early warning system and the algorithm using the same-percentage risk score cutoffs are similarly accurate. Both approaches successfully identify most of the students who ultimately are chronically absent, have a low grade point average, or fail a course. In contrast, the algorithm with 10-percent cutoffs is good at targeting the students who are most likely to experience an academic problem; this approach has the advantage in predicting suspensions, which are rarer and harder to predict than the other outcomes. Both the prior performance flags and the algorithm are less accurate when predicting outcomes for students who are Black.
The findings suggest clear tradeoffs between the options. The prior performance early warning system is just as accurate as the algorithm for some purposes and is cheaper and easier to set up, but it does not provide fine-grained information that could be used to identify the students who are at greatest risk. The algorithm can distinguish degrees of risk among students, enabling a district to set cutoffs that vary depending on the prevalence of different outcomes, the harms of over-identifying versus under-identifying students at risk, and the resources available to support interventions.
|2012–2016 Program for International Student Assessment Young Adult Follow-up Study (PISA YAFS): How reading and mathematics performance at age 15 relate to literacy and numeracy skills and education, workforce, and life outcomes at age 19
This Research and Development report provides data on the literacy and numeracy performance of U.S. young adults at age 19, as well as examines the relationship between that performance and their earlier reading and mathematics proficiency in PISA 2012 at age 15. It also explores how other aspects of their lives at age 19—such as their engagement in postsecondary education, participation in the workforce, attitudes, and vocational interests—are related to their proficiency at age 15.
|Are State Policy Reforms in Oregon Associated with Fewer School Suspensions and Expulsions?
In 2013 and 2015, Oregon enacted legislation that shifted school discipline policies from a zero-tolerance approach to one that emphasizes preventing behavioral problems and reducing unnecessary suspensions and expulsions. These types of discipline are often referred to as exclusionary because they remove students from classroom instruction. This study examines the association between state-level policies and suspension and expulsion rates in Oregon.
Study findings suggest that the policy shift has led to some short-term progress on two of the state’s main goals: reducing unnecessary removal of students from classroom instruction for disciplinary reasons and reducing exclusionary discipline for weapons offenses that do not involve firearms. Across all grade spans, the use of exclusionary discipline declined from 2008/09 to 2016/17 in Oregon schools, with higher reductions in the secondary grades. The declining rates of exclusionary discipline indicate progress, but growth in out-of-school suspensions in recent years suggests the need for further monitoring and additional support. For example, strengthening efforts to reduce suspensions for minor infractions, especially in secondary grades, could help reduce unnecessary suspensions overall&mdash:a priority of Oregon’s school discipline policy reforms.
|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.
|Getting students on track for graduation: Impacts of the Early Warning Intervention and Monitoring System after one year
Early warning systems that use research-based warning signs to identify students at risk of dropping out of high school have emerged as one strategy for improving graduation rates. This study tested the impact of one early warning system, the Early Warning Intervention and Monitoring System (EWIMS), on 37,671 students in grades 9 and 10 and their schools after one year of implementation. Seventy-three high schools were randomly assigned to implement EWIMS during the 2014/15 school year or to continue their usual practices for identifying and supporting students at risk of not graduating on time. Impact findings show that EWIMS reduced the percentage of students with risk indicators related to chronic absence and course failure but not related to low grade point averages, suspensions, or insufficient credits to graduate. At the school level, EWIMS did not have a detectable impact on school data culture, that is, the ways in which schools use data to make decisions and identify students in need of additional support. Findings suggest that overall implementation of the EWIMS seven-step process was low in nearly all EWIMS schools, and that implementation of EWIMS was challenging for participating schools. The authors hypothesize that other school-level processes, unmeasured in this study, also may have contributed to impacts on students. For example, effects might have emerged for chronic absence and course failure if schools prioritized encouraging students to show up and participate in their courses, even if they did not have a sophisticated set of interventions. Further research is needed to better understand the mechanisms through which EWIMS had an impact on chronic absence and course failure. This report provides rigorous, initial evidence that even with limited implementation during the first year of adoption, use of a comprehensive early warning system such as EWIMS can reduce the percentage of students who are chronically absent or who fail one or more courses. These short-term results are promising because chronic absence and course failures in grades 9 and 10 are two key indicators that students are off track for graduation.
|Stated Briefly: A comparison of two approaches to identify beating-the-odds high schools in Puerto Rico
This "Stated Briefly" report is a companion piece that summarizes the results of another report of the same name. 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.
|Race to the Top: Implementation and Relationship to Student Outcomes
Race to the Top (RTT), one of the Obama administration's signature programs and one of the largest federal government investments in an education grant program, received $4.35 billion in funding as part of the American Recovery and Reinvestment Act of 2009. Through three rounds of competition in 2010 and 2011, RTT awarded grants to states that agreed to implement a range of education policies and practices designed to improve student outcomes. Using 2013 interview data from all states, this report documents whether states that received an RTT grant used the policies and practices promoted by RTT and how that compares to non-grantee states. The report also examines whether receipt of an RTT grant was related to improvements in student outcomes. Findings show that 2010 RTT grantees reported using more policies and practices than non-grantees in four areas (standards and assessments, teachers and leaders, school turnaround, charter schools), and 2011 RTT grantees reported using more in one area (teachers and leaders). However, the relationship between RTT and student outcomes was not clear, as trends in test scores could be plausibly interpreted as providing evidence of either a positive, negative, or null effect for RTT.
|Identifying early warning indicators in three Ohio school districts
The purpose of this study was to identify a set of data elements for students in grades 8 and 9 in three Ohio school districts that could serve as accurate early warning indicators of their failure to graduate high school on time and to comparatively examine the accuracy of those indicators. In order to identify the set of indicators with the optimal accuracy for each district, the research team collected student-level data on two cohorts of grade 8 and 9 students in each school district. Datasets used in the analyses included students’ four-year graduation status (the outcome) and 8th and 9th grade data on attendance, coursework, suspensions, and test score records (the candidate early warning indicators). Logistic regression and Receiver Operating Characteristic (ROC) curve analyses were used to identify the candidate indicators that were the consistent predictors of students’ failure to graduate on time in each district and to identify the cut points on the original scales that most accurately distinguish students who were at risk of not graduating on time from those who did graduate on time. The analyses were restricted to students who were first-time freshmen within the districts in 2006/07 or 2007/08, and excluded students who entered the district after grade 9. Students in the 2006/07 cohort graduated in 2010, and students in the 2007/08 cohort graduated in 2011. The three districts included in the study varied in size, demographic composition, and locale. Results show that the optimal cut point for classifying students as at risk varied significantly across districts for five of the eight candidate indicators included in the study. Across the three districts and two grades, different indicators were identified as the most accurate predictors of students’ failure to graduate on time. End-of-year attendance rate was the only indicator that was a consistent predictor for both grades in all three districts. The most accurate indicators in both grade 8 and grade 9 were based on coursework (GPAs and course credits). Consistent with prior literature, failing more than one class and earning one or more suspensions also were strong predictors of failure to graduate on time. On average, indicators were more accurate in grade 9 than in grade 8. Findings illustrate why it is important for districts to conduct local validation using their own data to verify that indicators selected for their early warning systems accurately predict students’ failure to graduate on time. The methods laid out in this study can be used to help districts identify the best off-track indicators, and indicator cut points, for their particular early warning systems.
|School reading performance and the extended school day policy in Florida
Beginning with the 2012/13 school year, Florida law required that the 100 lowest-performing elementary schools in reading extend the school day. This study examined how the lowest performing schools implemented the extended school day policy and the trends in school reading performance among the lowest performing schools and other elementary schools. The lowest-performing schools were located throughout Florida and on average, were smaller but served higher proportions of minorities and higher proportions of students receiving free or reduced-price lunch compared to other elementary schools. The lowest-performing schools reported increasing the number of minutes of reading instruction provided to students, increasing staff, and providing different instruction in the extra hour than during other reading instructional blocks. An increase in reading performance was observed for the lowest-performing schools the year the extended school day was implemented. However, this increase did not exceed what would have been expected in the absence of the required increase in reading instruction.
|Comparing Methodologies for Developing an Early Warning System: Classification and Regression Tree Model Versus Logistic Regression
The purpose of this report was to explicate the use of logistic regression and classification and regression tree (CART) analysis in the development of early warning systems. It was motivated by state education leaders' interest in maintaining high classification accuracy while simultaneously improving practitioner understanding of the rules by which students are identified as at-risk or not at-risk readers. Logistic regression and CART were compared using data on a sample of grades 1 and 2 Florida public school students who participated in both interim assessments and an end-of-the year summative assessment during the 2012/13 academic year. Grade-level analyses were conducted and comparisons between methods were based on traditional measures of diagnostic accuracy, including sensitivity (i.e., proportion of true positives), specificity (proportion of true negatives), positive and negative predictive power, and overall correct classification. Results indicate that CART is comparable to logistic regression, with the results of both methods yielding negative predictive power greater than the recommended standard of .90. Details of each method are provided to assist analysts interested in developing early warning systems using one of the methods.
|A Practitioner's Guide to Implementing Early Warning Systems
To stem the tide of students dropping out, many schools and districts are turning to early warning systems (EWS) that signal whether a student is at risk of not graduating from high school. While some research exists about establishing these systems, there is little information about the actual implementation strategies that are being used across the country. This report summarizes the experiences and recommendations of EWS users throughout the United States.
|Review of Research on Student Nonenrollment and Chronic Absenteeism: A Report for the Pacific Region
In some areas of the Regional Educational Laboratory (REL) Pacific Region, between one-fourth and a half of secondary school–age students are not enrolled in school. Not being enrolled in school or being chronically absent can have lasting effects on students’ economic and social development. This REL Pacific report summarizes research on nonenrollment and chronic absenteeism from the United States and emergent nations that share characteristics with Pacific island nations. Four types of factors influence student nonenrollment and absenteeism: student-specific, family-specific, school-specific, and community-specific. Many of these potential factors are interconnected, and the effects of these factors may vary by region. Therefore, educators, policymakers, and family and community members in the Pacific Region may need to gather additional data in order to explore these factors in their own communities. Stakeholders can also use this review to begin to identify the root causes for why students are not in school in order to develop and implement targeted strategies to support student enrollment and attendance.