Search Results: (1-10 of 10 records)
|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.
|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.
|Forum Guide to Planning for, Collecting, and Managing Data About Students Displaced by a Crisis
The Forum Guide to Planning for, Collecting, and Managing Data About Students Displaced by a Crisis provides timely and useful best practice information for collecting and managing data about students who have temporarily or permanently enrolled in another educational setting because of a crisis. This guide updates the information included in the 2010 publication Crisis Data Management: A Forum Guide to Collecting and Managing Data about Displaced Students; highlights best practices that education agencies can adopt before, during, and after a crisis; and features case studies and real-world examples from agencies that have either experienced a crisis or received students who were displaced by a crisis.
|Indicators of School Crime and Safety: 2018
A joint effort by the National Center for Education Statistics and the Bureau of Justice Statistics, this annual report examines crime occurring in schools and colleges. This report presents data on crime at school from the perspectives of students, teachers, principals, and the general population from an array of sources—the National Crime Victimization Survey, the School Crime Supplement to the National Crime Victimization Survey, the Youth Risk Behavior Survey, the School Survey on Crime and Safety, the Schools and Staffing Survey, EDFacts, and the Campus Safety and Security Survey. The report covers topics such as victimization, bullying, school conditions, fights, weapons, the presence of security staff at school, availability and student use of drugs and alcohol, student perceptions of personal safety at school, and criminal incidents at postsecondary institutions.
|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.
|Beating the Odds: Finding Schools Exceeding Achievement Expectations with High-Risk Students
State education leaders are often interested in identifying schools that have demonstrated success with improving the literacy of students who are at the highest level of risk for reading difficulties. The identification of these schools that are “beating the odds” is typically accomplished by comparing a school’s observed performance on a particular exam, such as a state achievement exam, with how the school would be expected to perform when taking into account its demographic characteristics including the percentage of students classified as economically disadvantaged, minority, or as an English language learner. This study used longitudinal data from the Florida Department of Education on grade 3 public school students for the academic years 2010/11-2012/13 to determine which schools are exceeding student achievement expectations, and what demographic similarities exist between schools that are exceeding expectations and other schools.
|Evaluating the screening accuracy of the Florida Assessments for Instruction in
This report analyzed student performance on the FAIR reading comprehension screen across grades 4-10 and the Florida Comprehensive Assessment Test (FCAT) 2.0 to determine how well the FAIR and the 2011 FCAT 2.0 scores predicted 2012 FCAT 2.0 performance. The first key finding was that the reading comprehension screen of the Florida Assessments for Instruction in Reading (FAIR) was more accurate than the 2011 Florida Comprehensive Assessment Test (FCAT) 2.0 scores in correctly identifying students as not at risk for failing to meet grade-level standards on the 2012 FCAT 2.0. The second key finding was that using both the FAIR screen and the 2011 FCAT 2.0 lowered the underidentification rate of at-risk students by 12–20 percentage points compared with the results using the 2011 FCAT 2.0 score alone.
|High School Puente Program
The High School Puente Program aims to help disadvantaged students graduate from high school, become college eligible, and enroll in four-year colleges and universities. The program consists of the following components: 1) a 9th- and 10th-grade college preparatory English class that incorporates Mexican-American/Latino and other multicultural literature; 2) a four-year academic counseling program for students; and 3) student leadership and mentoring activities with volunteers from the local community. High School Puente is open to all students and is targeted to students from populations with low rates of enrollment at four-year colleges. Students are identified for the program at the end of their 8th-grade year through an application and selection process. Each High School Puente site is implemented by a team consisting of an academic counselor and an English teacher. These team members receive intensive initial training in program methodologies, along with ongoing training and support for as long as they implement the program. In addition to High School Puente, the Puente Program has a community college program model. The community college program does not fall within the WWC Dropout Prevention protocol.
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