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For the latest news and resources on the Every Student Succeeds Act (ESSA) visit our updated ESSA page. Information on No Child Left Behind, including the Act and policy, and the Obama Administration's blueprint for reauthorizing the Elementary and Secondary Education Act. ESEA Flexibility.
- ESEA Flexibility
ESEA Reauthorization:The Every Student Succeeds Act The U.S....
- Essa
Data; Every Student Succeeds Act (ESSA) Every Student...
- ESEA Flexibility
- Reporting
- Reading and Mathematics Assessment Data3
- Accountability Data
- 1.3 Current Issues in Data Quality
- 1.4 Document Overview and Organization
- 2.1 Overview: Data Quality and No Child Left Behind
- 2.2 Technical Infrastructure
- Current Initiatives.
- In the Field: Types of Data Systems
- Interim Processes. Of course, these
- Business Rules.
- In the Field: New Hampshire’s Data Dictionary
- General Principles
- 2.4 Staff Organization and Training
- In the Field: Empowering Data Stewards
- In the Field: Meeting the Data Quality Challenge in a Small LEA
- General Principles
- Guidelines for Specific NCLB Data Elements
- Does your data infrastructure have the following characteristics?
- 3. MANAGING CONSISTENT COLLECTION
- 3.1 Overview: Data Collection Processes
- 3.2 Collection Instruments
- In the Field: Rhode Island’s Data Portal
- Assessment Instruments. It is important to
- General Principles
- Guidelines for Specific NCLB Data Elements
- 3.3 Student Assessment Data Collection
- Working with Vendors on Assessment Quality Control: The TILSA SCASS Checklist
- Guidelines for Specific NCLB Data Elements
- Firm, Clear Schedules. Producing quality data
- Federal NCLB Reporting Timelines
- General Principles
- Guidelines for Specific NCLB Data Elements
- How would I put together a data collection schedule at the State level?
- Massachusetts Department of Education 2004-2005 Data Collection Reporting Schedule
- Does your data collection process have the following characteristics?
- Assessment instruments avoid direct student entry
- 4. CONFIRMING ACCURATE RESULTS
- 4.1 Overview: Data Quality Management Controls
- 4.2 Data Review and Validation
- Cleaning the Aggregate. After initial
- General Principles
- Guidelines for Specific NCLB Data Elements
- 4.3 Data Privacy and Security Issues
- General Principles
- Does your data validation process have the following characteristics?
- Data systems include embedded security safeguards
- Ensuring
- 5.1 Roles and Responsibilities
- APPENDIX: SUMMARY CHECKLISTS
- Does your data infrastructure have the following characteristics?
- Does your data collection process have the following characteristics?
- Assessment instruments avoid direct student entry
- Does your data validation process have the following characteristics?
- Data systems include embedded security safeguards
State- LEA- Major Students Limited Econom-High Low School All Stu-
Percentage of students tested Percentage of students achieving at each proficiency level Most recent 2-year trend data in student achievement for each subject and grade assessed LEA achievement compared to State achievement School achievement compared to LEA and State achievement
Comparison between actual achievement and State's annual measurable objectives Student achievement on other academic indicators used for AYP (e.g., high school graduation rate) Number and names of LEAs and schools identified for improvement, corrective action, and restructuring Percentage of schools identified for school improvement, corrective act...
NCLB’s greatly enhanced focus on data-driven accountability has brought with it a number of challenges for States, LEAs, and schools. Federal NCLB reporting requires that States have the capability to transmit standard statewide information on demographics, achievement, and teacher quality for all public school students and all public school teach...
This document focuses on the processes and mechanisms of data collection and reporting – not the substance or content of particular data elements. The purpose of these guidelines is to help States, LEAs, and schools establish sound systems conducive to producing accurate, reliable data. The purpose is not to identify the types of data that should...
Within the confines of this document, the definition of “data quality” encompasses two of the three components of OMB’s overarching definition: objectivity and integrity. These guidelines assume that the data elements required by NCLB and by States are, by definition, useful in measuring progress toward predefined Federal and State accountability...
Automated Systems. Having an adequate technical infrastructure in place is one key element in producing quality data. At a minimum, data collection, processing, and reporting should be automated and transmittable in an electronic format. Even in small States, LEAs, and schools, pen-and-paper systems for managing data will be overwhelmed by the e...
The range of technology options available to States, LEAs, and schools in automating data collection processes is vast – from inexpensive desktop spreadsheets to fully integrated State data warehouses linked to every school. Driven largely by NCLB’s requirements, numerous ambitious Federal and State initiatives are currently underway to implement ...
Three different types of automated student data systems exist that promote data quality by State Educational Agencies (SEAs). West Virginia and Delaware host student information systems that are used by LEAs and schools on a day-to-day basis. When data are needed for reporting, the SEA can download what is needed from the real-time systems and rec...
systems are complicated to develop and take time to complete. However, data, assessment, and accountability professionals at the State and local levels should not postpone steps to improve data quality while they wait for a fully automated, fully integrated statewide data system to be implemented. Several technical infrastructure practices that w...
A collection and reporting system that is linked directly to a data dictionary can greatly improve data quality as it funnels – or, in some cases, forces – data into a pre-defined configuration. This integration is achieved through the creation of
As part of the U.S. Department of Education’s Data Quality and Standards Project, New Hampshire has begun to implement the “i.4.see” system, an automated education information database. Working with the Center for Data Quality (C4DQ), New Hampshire has established an online data dictionary that lists the definitions, data rules, and validation req...
Unique Identifiers: To the maximum extent possible, unique statewide identifiers should attach to every student and teacher for whom NCLB data are required. Indivisibility: Every data element should be defined and collected in as “granular” a format as possible. For example, the data dictionary should separate total days in membership and tota...
The Data Quality Team. As important as a solid technical infrastructure and a data dictionary are to producing quality data, it is people who determine whether or not NCLB and other data reporting meets a high standard of accuracy. Automation, interoperability, and connectivity of information technology can provide a framework for producing good...
When Virginia’s Fairfax County Public School System began its push for improved data quality through the Education Decision Support Library, a key element in its approach was that the consumers of the data would drive the system, rather than the technology staff. Fairfax designated “data stewards” at every school, who assumed ownership over specif...
The process of training and organizing staff to establish an efficient, effective data quality team can be a daunting task for any LEA or school. For small and/or rural LEAs with limited resources and administrative staff of only a few people, the challenge is magnified. As a result, some small schools and LEAs take a “we just can’t do it” approa...
Organization: Designate dedicated staff at the State, LEA, and school levels with specific responsibility for and authority over monitoring data quality. State level: establish a chief information officer, a data quality office, and a data policy advisory committee. LEA level: establish a chief information officer, a data quality coordinator, and...
NCLB Demographic Data Designate a single data steward in each school who is responsible for ensuring that data are entered according to standard definitions. Train all school and LEA data staff on the Federal definitions of each of the required NCLB subgroup categories. Train all data staff in the relationship between NCLB subgroup classifications ...
Data collection, processing, and reporting systems are automated and data can be transmitted in an electronic, interoperable format. Immediate interim processes for improving data quality are in place, as larger systemic initiatives are implemented. A data dictionary identifies all data elements used in collection and reporting, and describes the...
As Section 2 showed, preventing data errors before they occur is one crucial component to improving data quality. A solid technical infrastructure, a comprehensive data dictionary, clear documentation of accountability measures, and an organizational culture of data quality can greatly reduce the potential for data problems. However, there are a...
Mining Existing Data. Many of the data required for State- and LEA-level reporting and for the annual NCLB report cards are available from existing data sources (such as individual student record systems), and do not require a separate “collection.” Rather, these data can be “mined” by examining current databases and extracting the relevant info...
Instrument Design. Managing a consistent data collection process begins with well-designed collection instruments. Poorly-prepared collection tools can put data quality at risk before a single piece of data is collected or entered into a database. In the case of NCLB Report Card data, there are a number of different types of collection instrumen...
The Rhode Island Department of Elementary and Secondary Education has implemented a one-stop data collection, entry, and reporting portal. Accessible by password through the Department’s public website, the portal contains not only data submission and reporting forms, but also a fully functional data dictionary, data validation rules, and State da...
note that a student’s assessment answer sheet is often a data collection tool in itself. Assessment systems in which students fill in “bubble sheets” or enter answers into an electronic database are, essentially, asking students to enter their own “achievement indicators” into the system. This makes the clarity of the answer sheet’s instructions ...
Relevance: Collection instruments should request only information directly appropriate to the specific reporting requirement or data need under consideration. Before converting a paper form to electronic format, do a data audit to determine what is necessary and what is outdated or redundant. The key question is “what do we need, both now and in...
NCLB Demographic Data Wherever possible, use existing data sources to collect information on students’ subgroup affiliation. New collection instruments should not be necessary if existing instruments can be automatically mined for these data. Ensure that data in the student demographic information system are current prior to assessment. Pay parti...
throughout all collection, entry, and reporting processes. Initial data validation consists of automated quality checks that ensure data are in the proper format. Data stewards clean aggregated data to flag out-of-range errors and confirm reported data “make sense.” A systematic follow-up process is in place to correct questionable data before r...
throughout all collection, entry, and reporting processes. Initial data validation consists of automated quality checks that ensure data are in the proper format. Data stewards clean aggregated data to flag out-of-range errors and confirm reported data “make sense.” A systematic follow-up process is in place to correct questionable data before r...
throughout all collection, entry, and reporting processes. Initial data validation consists of automated quality checks that ensure data are in the proper format. Data stewards clean aggregated data to flag out-of-range errors and confirm reported data “make sense.” A systematic follow-up process is in place to correct questionable data before r...
throughout all collection, entry, and reporting processes. Initial data validation consists of automated quality checks that ensure data are in the proper format. Data stewards clean aggregated data to flag out-of-range errors and confirm reported data “make sense.” A systematic follow-up process is in place to correct questionable data before r...
throughout all collection, entry, and reporting processes. Initial data validation consists of automated quality checks that ensure data are in the proper format. Data stewards clean aggregated data to flag out-of-range errors and confirm reported data “make sense.” A systematic follow-up process is in place to correct questionable data before r...
throughout all collection, entry, and reporting processes. Initial data validation consists of automated quality checks that ensure data are in the proper format. Data stewards clean aggregated data to flag out-of-range errors and confirm reported data “make sense.” A systematic follow-up process is in place to correct questionable data before r...
throughout all collection, entry, and reporting processes. Initial data validation consists of automated quality checks that ensure data are in the proper format. Data stewards clean aggregated data to flag out-of-range errors and confirm reported data “make sense.” A systematic follow-up process is in place to correct questionable data before r...
throughout all collection, entry, and reporting processes. Initial data validation consists of automated quality checks that ensure data are in the proper format. Data stewards clean aggregated data to flag out-of-range errors and confirm reported data “make sense.” A systematic follow-up process is in place to correct questionable data before r...
throughout all collection, entry, and reporting processes. Initial data validation consists of automated quality checks that ensure data are in the proper format. Data stewards clean aggregated data to flag out-of-range errors and confirm reported data “make sense.” A systematic follow-up process is in place to correct questionable data before r...
throughout all collection, entry, and reporting processes. Initial data validation consists of automated quality checks that ensure data are in the proper format. Data stewards clean aggregated data to flag out-of-range errors and confirm reported data “make sense.” A systematic follow-up process is in place to correct questionable data before r...
throughout all collection, entry, and reporting processes. Initial data validation consists of automated quality checks that ensure data are in the proper format. Data stewards clean aggregated data to flag out-of-range errors and confirm reported data “make sense.” A systematic follow-up process is in place to correct questionable data before r...
throughout all collection, entry, and reporting processes. Initial data validation consists of automated quality checks that ensure data are in the proper format. Data stewards clean aggregated data to flag out-of-range errors and confirm reported data “make sense.” A systematic follow-up process is in place to correct questionable data before r...
throughout all collection, entry, and reporting processes. Initial data validation consists of automated quality checks that ensure data are in the proper format. Data stewards clean aggregated data to flag out-of-range errors and confirm reported data “make sense.” A systematic follow-up process is in place to correct questionable data before r...
throughout all collection, entry, and reporting processes. Initial data validation consists of automated quality checks that ensure data are in the proper format. Data stewards clean aggregated data to flag out-of-range errors and confirm reported data “make sense.” A systematic follow-up process is in place to correct questionable data before r...
throughout all collection, entry, and reporting processes. Initial data validation consists of automated quality checks that ensure data are in the proper format. Data stewards clean aggregated data to flag out-of-range errors and confirm reported data “make sense.” A systematic follow-up process is in place to correct questionable data before r...
throughout all collection, entry, and reporting processes. Initial data validation consists of automated quality checks that ensure data are in the proper format. Data stewards clean aggregated data to flag out-of-range errors and confirm reported data “make sense.” A systematic follow-up process is in place to correct questionable data before r...
throughout all collection, entry, and reporting processes. Initial data validation consists of automated quality checks that ensure data are in the proper format. Data stewards clean aggregated data to flag out-of-range errors and confirm reported data “make sense.” A systematic follow-up process is in place to correct questionable data before r...
throughout all collection, entry, and reporting processes. Initial data validation consists of automated quality checks that ensure data are in the proper format. Data stewards clean aggregated data to flag out-of-range errors and confirm reported data “make sense.” A systematic follow-up process is in place to correct questionable data before r...
throughout all collection, entry, and reporting processes. Initial data validation consists of automated quality checks that ensure data are in the proper format. Data stewards clean aggregated data to flag out-of-range errors and confirm reported data “make sense.” A systematic follow-up process is in place to correct questionable data before r...
throughout all collection, entry, and reporting processes. Initial data validation consists of automated quality checks that ensure data are in the proper format. Data stewards clean aggregated data to flag out-of-range errors and confirm reported data “make sense.” A systematic follow-up process is in place to correct questionable data before r...
throughout all collection, entry, and reporting processes. Initial data validation consists of automated quality checks that ensure data are in the proper format. Data stewards clean aggregated data to flag out-of-range errors and confirm reported data “make sense.” A systematic follow-up process is in place to correct questionable data before r...
throughout all collection, entry, and reporting processes. Initial data validation consists of automated quality checks that ensure data are in the proper format. Data stewards clean aggregated data to flag out-of-range errors and confirm reported data “make sense.” A systematic follow-up process is in place to correct questionable data before r...
throughout all collection, entry, and reporting processes. Initial data validation consists of automated quality checks that ensure data are in the proper format. Data stewards clean aggregated data to flag out-of-range errors and confirm reported data “make sense.” A systematic follow-up process is in place to correct questionable data before r...
throughout all collection, entry, and reporting processes. Initial data validation consists of automated quality checks that ensure data are in the proper format. Data stewards clean aggregated data to flag out-of-range errors and confirm reported data “make sense.” A systematic follow-up process is in place to correct questionable data before r...
throughout all collection, entry, and reporting processes. Initial data validation consists of automated quality checks that ensure data are in the proper format. Data stewards clean aggregated data to flag out-of-range errors and confirm reported data “make sense.” A systematic follow-up process is in place to correct questionable data before r...
throughout all collection, entry, and reporting processes. Initial data validation consists of automated quality checks that ensure data are in the proper format. Data stewards clean aggregated data to flag out-of-range errors and confirm reported data “make sense.” A systematic follow-up process is in place to correct questionable data before r...
throughout all collection, entry, and reporting processes. Initial data validation consists of automated quality checks that ensure data are in the proper format. Data stewards clean aggregated data to flag out-of-range errors and confirm reported data “make sense.” A systematic follow-up process is in place to correct questionable data before r...
throughout all collection, entry, and reporting processes. Initial data validation consists of automated quality checks that ensure data are in the proper format. Data stewards clean aggregated data to flag out-of-range errors and confirm reported data “make sense.” A systematic follow-up process is in place to correct questionable data before r...
throughout all collection, entry, and reporting processes. Initial data validation consists of automated quality checks that ensure data are in the proper format. Data stewards clean aggregated data to flag out-of-range errors and confirm reported data “make sense.” A systematic follow-up process is in place to correct questionable data before r...
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4 days ago · The controversial No Child Left Behind Act (NCLB) brought test-based school accountability to scale across the United States. This study draws together results from multiple data sources to ...
Oct 27, 2015 · The Elementary and Secondary Education Act hasn't been updated since it was renamed "No Child Left Behind" in 2001 by President George W. Bush. The law was introduced by President Lyndon Johnson ...
Aug 25, 2011 · The No Child Left Behind (NCLB) Act was intended to promote higher levels of performance in U.S. public education by tying a school’s federal funding directly to student achievement as measured by standardized test scores. Ten years after its implementation, however, research on NCLB suggests that the achievement levels of the nation’s ...
Apr 18, 2023 · No Child Left Behind revolutionized national student data collection. A new initiative from the U.S. Chamber of Commerce Foundation, The Future of Data, Assessments, and Accountability in K-12 Education initiative, is exploring the effectiveness of data and assessments in America’s K-12 public schools. The first phase of the project examines ...
Every Last Child: Many of the children most at risk of being left behind by progress in health, education or protection are not represented in household surveys despite recent efforts to improve survey sampling and to include more population groups in surveys. Qualitative data collected through dedicated research projects, advocacy work or ...
