ethanporcaro/eric-fulltext-meta huggingface dataset:
Facts for Teachers, 2015
Author: National Center for Education Statistics (ED)
Date: 2015-00-00
Type: Reports - Descriptive
Subjects: National Competency Tests,Testing,Test Results,Grade 4,Grade 8,Grade 12

The National Assessment of Education Progress (NAEP) is the largest continuing and nationally representative assessment of what students know and can do in various subjects. NAEP serves a different role than state assessments. States have their own assessments that are designed to provide individual student data about achievement on different content standards. The National Center for Education Statistics (NCES), within the U.S. Department of Education, administers NAEP as mandated by Congress and does so in every state. This provides a common measure of achievement that allows for comparisons of achievement to the nation and participating urban districts. The results of NAEP are released to the public as The Nation's Report Card. Teachers are essential partners in NAEP. Teachers promote understanding of what influences student learning and can use NAEP questions as a resource in the classroom. This brief document highlights important dates and information about the 2015 administration of NAEP, how NAEP reports results, and recent NAEP findings. [This publication was prepared for the National Assessment of Educational Progress by Hager Sharp under contract to the National Center for Education Statistics.]
From Good to Great: Discussion Starter Tool
Author: Center on Great Teachers and Leaders at American Institutes for Research
Date: 2014-04-00
Type: Reports - Research
Subjects: Teacher Effectiveness,Teacher Surveys,Novices,Teaching Experience,Teacher Leadership,Practicums,Mentors,Teacher Certification,School Culture,Teacher Attitudes,Teacher Education,Educational Research,Masters Degrees,Teacher Evaluation,Teacher Researchers,Teacher Improvement,Professional Development,Teacher Educators

In the report &quot;From Good to Great: Exemplary Teachers Share Perspectives on Increasing Teacher Effectiveness across the Career Continuum,&quot; (See full report in ERIC at ED555657) National and State Teachers of the Year shared their views on what helped them become great teachers. This accompanying &quot;Discussion Starter Tool&quot; builds on the report's findings to help policy leaders, school leaders, and--especially--teachers and teacher leaders generate constructive dialogue about the types of supports that should be made available to help all teachers become more effective. The report and this accompanying tool are the product of a year-long collaboration among seven leading organizations working to advance teaching and elevate the profession. [Proposed by the National Network of State Teachers of the Year (NNSTOY) and led by the Center on Great Teachers and Leaders (GTL Center) at American Institutes for Research, the study involved the following partner organizations: American Association of Colleges of Teacher Education, Council for the Accreditation of Educator Preparation, Council of Chief State School Officers, National Council on Teacher Quality, and National Education Association.]
Facts for Districts
Author: National Center for Education Statistics (ED)
Date: 2015-00-00
Type: Reports - Descriptive
Subjects: School Districts,Educational Assessment,Mathematics,Reading,Sciences,Writing (Composition),Academic Achievement,Computer Assisted Testing,Elementary Secondary Education,Grade 4,Grade 8,Grade 12,Educational Quality,Demography

The National Assessment of Education Progress (NAEP) is the largest nationally representative assessment of what the nation's students know and can do in subjects such as mathematics, reading, science, and writing. The results of NAEP are released as The Nation's Report Card. The National Center for Education Statistics (NCES), within the U.S. Department of Education, administers NAEP as mandated by Congress and does so in every state. This provides a common measure of achievement. District staff play an essential role in NAEP. Superintendents and district staff can work closely with principals to explain the importance of the assessment to teachers and students selected for participation. When students take part in NAEP and give their best effort, the outcome is the most accurate measure possible of student achievement across the country. In 2015, NAEP will begin to transition the mathematics, reading, and science assessment from paper and pencil to technology-based assessments. This document briefly describes the changes that will take place and highlights the important and useful features of NAEP for district and school staff. [This publication was prepared for the National Assessment of Educational Progress by Hager Sharp under contract to the National Center for Education Statistics.]
Student Growth Percentiles Based on MIRT: Implications of Calibrated Projection. CRESST Report 842
Author: Monroe, Scott
Date: 2014-09-00
Type: Reports - Research
Subjects: Item Response Theory,Achievement Gains,Regression (Statistics),Error of Measurement,Scores,Comparative Analysis,Computation

This research concerns a new proposal for calculating student growth percentiles (SGP, Betebenner, 2009). In Betebenner (2009), quantile regression (QR) is used to estimate the SGPs. However, measurement error in the score estimates, which always exists in practice, leads to bias in the QR-­based estimates (Shang, 2012). One way to address this issue is to estimate the SGPs using a modeling framework that can directly account for the measurement error. Multidimensional IRT (MIRT) is one such framework, and the one utilized here. To maximize the generality of the approach, the SNP-­MIRT model (Monroe, 2014), which estimates the shape of the latent variable density, is used to obtain model parameter estimates. These estimates are then used with the calibrated projection linking methodology (Thissen, Varni, et al., 2011, Thissen, Liu, Magnus, &amp; Quinn, 2014, Cai, in press-­a, Cai, in-­press-­b) to produce SGP estimates. The methods are compared using simulated and empirical data.
Dynamic Bayesian Network Modeling of Game Based Diagnostic Assessments. CRESST Report 837
Author: Levy, Roy
Date: 2014-01-00
Type: Reports - Descriptive
Subjects: Video Games,Educational Games,Bayesian Statistics,Observation,Evaluation Methods,Inferences,Student Evaluation,Diagnostic Tests,Models,Misconceptions,Psychometrics,Performance Based Assessment,Addition,Mathematics Skills,Problem Solving,Grade 6,Grade 7,Grade 8,Markov Processes,Monte Carlo Methods

Digital games offer an appealing environment for assessing student proficiencies, including skills and misconceptions in a diagnostic setting. This paper proposes a dynamic Bayesian network modeling approach for observations of student performance from an educational video game. A Bayesian approach to model construction, calibration, and use in facilitating inferences about students on the fly is described.
Estimation of a Ramsay-Curve Item Response Theory Model by the Metropolis-Hastings Robbins-Monro Algorithm. CRESST Report 834
Author: Monroe, Scott
Date: 2013-09-00
Type: Reports - Research
Subjects: Item Response Theory,Maximum Likelihood Statistics,Statistical Inference,Models,Measurement Techniques,Measurement Objectives,Computation,Multidimensional Scaling,Nonparametric Statistics,Goodness of Fit,Simulation,Data Analysis,Evidence,Mathematical Applications,Monte Carlo Methods

In Ramsay curve item response theory (RC-IRT, Woods &amp; Thissen, 2006) modeling, the shape of the latent trait distribution is estimated simultaneously with the item parameters. In its original implementation, RC-IRT is estimated via Bock and Aitkin's (1981) EM algorithm, which yields maximum marginal likelihood estimates. This method, however, does not produce the parameter covariance matrix as an automatic byproduct upon convergence. In turn, researchers are limited in when they can employ RC-IRT, as the covariance matrix is needed for many statistical inference procedures. The present research remedies this problem by estimating the RC-IRT model parameters by the Metropolis-Hastings Robbins-Monro (MH-RM, Cai, 2010) algorithm. An attractive feature of MH-RM is that the structure of the algorithm makes estimation of the covariance matrix convenient. Additionally, MH-RM is ideally suited for multidimensional IRT, whereas EM is limited by the &quot;curse of dimensionality.&quot; Based on the current research, when RC-IRT or similar semi-nonparametric IRT models are eventually generalized to include multiple latent dimensions, MH-RM would appear to be the logical choice for estimation. Complete Data Log-Likelihood and Derivatives for the Ramsay Curve is appended.
A New Statistic for Evaluating Item Response Theory Models for Ordinal Data. CRESST Report 839
Author: Cai, Li
Date: 2014-03-00
Type: Reports - Evaluative
Subjects: Item Response Theory,Models,Goodness of Fit,Probability,Statistical Analysis,Outcome Measures,Measurement Techniques,Measurement,Statistics,Computation,Access to Information,Maximum Likelihood Statistics,Statistical Distributions,Statistical Data,Tests,Error of Measurement,Evidence,Mathematical Applications,Evaluation Methods,Statistical Studies

We propose a new limited-information goodness of fit test statistic C[subscript 2] for ordinal IRT models. The construction of the new statistic lies formally between the M[subscript 2] statistic of Maydeu-Olivares and Joe (2006), which utilizes first and second order marginal probabilities, and the M*[subscript 2] statistic of Cai and Hansen (2013), which collapses the marginal probabilities into means and product moments. Unlike M*[subscript 2], C[subscript 2] may be computed even when the number of items is small and the number of categories is large. It is as well calibrated as the alternatives and can be more powerful than M[subscript 2]. When all items are dichotomous, C[subscript 2] becomes equivalent to M*[subscript 2], which is also equivalent to M[subscript 2]. We analyze empirical data from a patient-reported outcomes measurement development project to illustrate the potential differences in substantive conclusions that one may draw from the use of different statistics for model fit assessment.
Confessions of an At Risk Teacher: I Can't Imagine a Job Any Harder Than This!
Author: Costley, Kevin C.
Date: 2015-04-05
Type: Reports - Descriptive
Subjects: Teaching (Occupation),At Risk Students,Student Behavior,Poverty,Family Structure,Early Intervention,Student Mobility,Grade 1,Elementary School Students,Low Achievement,Achievement Gap,Teaching Conditions,Prior Learning,Social Influences,Behavior Problems,Discipline,Family Influence

Poverty has always been a problem in the United States and no doubt the condition unfortunately will always prevail. Title One schools will always have children enrolled who come from the lower socioeconomic class. This article has to do with the devastating effect of poverty, including stories from a classroom teacher of students who lived in homes of instability. Much of the article includes examples of occurrences in and out of the classroom that startled the writer of this article writer (as a first grade teacher). The teacher noticed some disturbing patterns of behavior with this population of children. Students who came from poor homes often had only one parent and rarely two. Some children were being raised by one or more grandparents who were doing their best to raise their grandchildren. Some fathers were in prison. Some children lived in blended home. A great majority of children lived in slumlord apartments. Many lived with just one parent. Many students were mobile and never stayed at any school very long. Most students, even those who previously attended Head Start program were still greatly behind in being ready for school. The first grade teacher (the writer of this article) spent the first few weeks catching up students on prior knowledge experiences including content deficits.. The teacher had to seek knowledge of other teachers in the school in order to understand these children's backgrounds. This article is a written response to a pre-service teacher from a nearby university requesting that the author of this article's input on at-risk children.
Trends in Graduate Student Financing: Selected Years, 1995-96 to 2011-12. Web Tables. NCES 2015-026
Author: National Center for Education Statistics (ED)
Date: 2015-01-00
Type: Numerical/Quantitative Data
Subjects: Trend Analysis,Graduate Students,Federal Aid,Student Financial Aid,Tables (Data),Annual Reports,Educational Research,Student Loan Programs,Financial Policy,Finance Reform,Demography,Student Characteristics,Student Costs,Enrollment Trends,Statistical Data,Statistical Distributions,Statistical Surveys,Predictor Variables,Error of Measurement,Masters Degrees,Doctoral Degrees,Age Differences,Gender Differences,Institutional Characteristics,Tuition,Fees,Racial Differences,Ethnicity,Income,Marital Status,Intellectual Disciplines,Attendance

In 2011-12, graduate students received a total of $51.7 billion in federal loans and grants, institutional grants, employer support, and financial aid from other sources. In 2007-08, this figure was $36.7 billion (College Board 2008, 2012). The data presented in these Web Tables were collected through five administrations of the National Postsecondary Student Aid Study (NPSAS:96, NPSAS:2000, NPSAS:04, NPSAS:08, and NPSAS:12). During that time, there have been several significant changes in federal financial aid that have affected graduate students; Graduate and Professional Student PLUS Loans were introduced in 2006 and the Unsubsidized Direct (Stafford) Loan limits were increased for graduate students in 2007. As of July 2012, after the latest survey period ended, graduate students were no longer able to receive Subsidized Direct Loans. These Web Tables provide detailed estimates on the sources of funds that graduate students used to finance their education over a 16-year period, beginning with NPSAS:96.1 All the data are presented separately for students in the largest graduate degree programs and by demographic and other academic characteristics for the 5 survey years from 1995-96 to 2011-12. Section 1 introduces trends in the composition of graduate students, the cost of their education, how much aid they received, and in what combinations. Section 2 focuses on trends in graduate students' receipt of graduate financial aid, showing the percentages who received each major type of financial aid and the average amounts they received in current dollars, by selected enrollment and student characteristics. Section 3 focuses on trends in borrowing by graduate students and how their borrowing is related to the receipt of other aid, to the maximum amounts borrowed, and to total borrowing.
Enrollment in Postsecondary Institutions, Fall 2013; Financial Statistics, Fiscal Year 2013; and Employees in Postsecondary Institutions, Fall 2013. First Look (Provisional Data). NCES 2015-012
Author: Ginder, Scott A.
Date: 2014-10-00
Type: Reports - Evaluative
Subjects: Enrollment Trends,Postsecondary Education,Higher Education,Educational Finance,Human Resources,Student Characteristics,Expenditures,Employees,School Personnel,Federal Legislation,Educational Legislation,Undergraduate Students,Graduate Students,Income,Racial Differences,Ethnic Groups,Attendance,Gender Differences,Full Time Students,Part Time Students,Institutional Characteristics,Private Colleges,Public Colleges,Operating Expenses,Tuition,Fees,Grants,Student Financial Aid,College Faculty,African American Students,Asian American Students,American Indian Students,Alaska Natives,Hawaiians,Pacific Islanders,Hispanic American Students,Multiracial Persons

This &quot;First Look&quot; presents findings from the provisional data of the Integrated Postsecondary Education Data System (IPEDS) spring 2014 data collection, which included three survey components: (1) Enrollment at postsecondary institutions during fall 2013; (2) Finance, for the 2013 fiscal year; and (3) Human Resources at postsecondary institutions during fall 2013. Data for all components were collected through the IPEDS web-based data collection system. This &quot;First Look&quot; provides users with an opportunity to access fully reviewed, edited, and imputed IPEDS data. Final data, including revisions to the provisional data submitted by institutions after the close of data collection, will be available during the following collection year (2014-15). The purpose of this report is to introduce new data through the presentation of tables containing descriptive information. Selected findings have been chosen to demonstrate the range of information available when using the IPEDS data rather than to discuss all of the observed differences. The following are appended: (1) Data Collection Procedures; and (2) Glossary of IPEDS Terms.
Data Collection and Use in Early Childhood Education Programs: Evidence from the Northeast Region. REL 2015-084
Author: Zweig, Jacqueline
Date: 2015-04-00
Type: Reports - Research
Subjects: Preschool Education,Data Collection,Early Childhood Education,Educational Quality,Decision Making,Evaluation Utilization,Administrators,Preschool Teachers,Structured Interviews,Coding,Thematic Approach,Student Evaluation,Teacher Student Relationship,Preschool Children,Performance Based Assessment,Attendance Patterns,Observation,Teaching Methods,Scores,Age Differences,School Schedules,Family Income,Institutional Characteristics,Student Characteristics,Private Schools,Public Schools,Child Development,Classroom Environment,Reading Achievement,Social Development,Emotional Development,At Risk Students,Information Dissemination,Parents,Competence,Information Management,Racial Differences,Ethnic Groups,Language Usage,Teacher Characteristics

The purpose of this study is to describe what data preschools in a mid-sized city in the Northeast collect, how they use that data, and the challenges they face. The study focuses on three types of data: early learning outcomes, dosage (i.e., the amount of exposure to early childhood education), and classroom quality. The report further demonstrates how preschools could use data to inform program-level decisions. Administrators and teachers at seven preschools in a convenience sample of preschools were interviewed using a structured protocol. The transcripts were coded using hierarchically organized codes and then analyzed to determine the main themes. The report also includes descriptive analyses of early learning outcomes, dosage, and classroom quality data provided by two of the preschool programs. Participating preschools assess children's early learning outcomes through a variety of systems to inform instruction and for parent outreach. Preschools report various processes for collecting and storing attendance data for compliance purposes, but some want to link them to learning outcomes. Participating preschools conduct observations of classrooms to provide feedback to teachers. There are some challenges to combining and analyzing individual programs' early learning and dosage data to inform program-level decisions. Further research is needed to determine which systems for collecting and using data lead to better outcomes for children. Early childhood programs may benefit from guidance on effective methods for presenting information about children's progress and the importance of attending preschool to parents and other relevant audiences. The following are appended: (1) Methodology for interviews; (2) Methodology for data analysis; (3) Process to combine data; (4) Administrator interview protocol; and (5) Teacher interview protocol. [For a summary of this report, see &quot;Data Collection and Use in Early Childhood Education Programs: Evidence from the Northeast Region. Stated Briefly. REL 2015-085&quot; (ED555738).]
Data Collection and Use in Early Childhood Education Programs: Evidence from the Northeast Region. Stated Briefly. REL 2015-085
Author: Zweig, Jacqueline
Date: 2015-04-00
Type: Reports - Research
Subjects: Preschool Education,Data Collection,Early Childhood Education,Educational Quality,Decision Making,Evaluation Utilization,Administrators,Preschool Teachers,Structured Interviews,Coding,Thematic Approach,Student Evaluation,Teacher Student Relationship,Preschool Children,Performance Based Assessment,Attendance Patterns,Observation,Teaching Methods,Scores,Age Differences,School Schedules,Family Income,Institutional Characteristics,Student Characteristics,Private Schools,Public Schools,Child Development,Classroom Environment,Reading Achievement,Social Development,Emotional Development,At Risk Students,Information Dissemination,Parents,Competence,Information Management,Racial Differences,Ethnic Groups,Language Usage,Teacher Characteristics

This &quot;Stated Briefly&quot; report is a companion piece that summarizes the results of another report of the same name. This study explores how seven early childhood education programs in a mid-sized city in the Northeast region are collecting and using data, how they would like to use data, how they could use the data that they have, and the challenges they face in these efforts. Participating preschools used a variety of externally and internally developed systems to collect data on early learning outcomes, dosage (the amount of time children spend in early childhood education), and classroom quality. The preschools also provided data on early learning outcomes and dosage to parents, but some administrators and teachers had concerns about effective strategies for communicating findings from the data. The preschools reported collecting sufficient data and generally do not want to collect more data. [For the full report, see &quot;Data Collection and Use in Early Childhood Education Programs: Evidence from the Northeast Region. REL 2015-084&quot; (ED555737).]
Toolkit for a Workshop on Building a Culture of Data Use. REL 2015-063
Author: Gerzon, Nancy
Date: 2015-04-00
Type: Guides - Non-Classroom
Subjects: Data,Research Utilization,School Districts,Schools,Instructional Materials,Workshops,Facilitators (Individuals),Interpersonal Communication,Resources,Professional Development,Leadership,Visual Aids,Information Literacy,Printed Materials,Organizational Culture,Elementary Secondary Education

The Culture of Data Use Workshop Toolkit helps school and district teams apply research to practice as they establish and support a culture of data use in their educational setting. The field-tested workshop toolkit guides teams through a set of structured activities to develop an understanding of data-use research in schools and to analyze examples from practice. Teams learn to apply these concepts to enhance their own culture of data use and outline effective next steps. The conceptual framework of the toolkit draws on five research-based elements known to support an effective culture of data use, and supporting materials--a facilitator's guide and agenda, a slide deck, and participant handouts--provide workshop facilitators with all the necessary materials to lead this process in their own setting.