The Data Model Task Force of the National Forum on Education Statistics has created a pk-12 data model for education. The Education Data Model (Version I) represents the information that schools, LEAs, and states would want to collect and manage to meet the education needs of their students and to maintain effective organizations in the service of those needs. A single, comprehensive model of education data is a prerequisite to establishing automated systems with the right, accurate data that are comparable across time and systems. In this session, the Education Data Model and its application were presented, leading to a discussion by session participants on how to use the Education Data Model to develop and improve longitudinal data systems.

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Using the Education Data Model in Longitudinal Data Systems (5.55 MB)

Sessions in LDS track:

I-A, II-A, III-A, IV-A, V-A, VI-A, VII-A, VIII-A, IX-A, X-A, XI-A, and XI-D

Teachers are the focus of the West Virginia Board of Education goal of improving instruction and learning through the use of technology and data. The state is rolling out an information solution to help teachers toward that goal. This presentation illustrated how the West Virginia Education Information System, designed for all schools in the state, is becoming more open to allow teacher access to student information that has previously been inaccessible.

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Making a Statewide Data System Work for Teachers (2.30 MB)

Sessions in Statewide LDS track:

I-B, II-B, III-B, IV-B, V-B, VI-B, VII-B, VIII-B, IX-B, X-B, and XI-B

Since 1968, the U.S. Department of Education has implemented the Civil Rights Data Collection (CRDC) to collect school- and district-level data on key education and civil rights issues in our nation's public schools. This session focused on the upcoming 2008 CRDC, which will collect data from school years 2007–08 and 2008–09 from a sample of approximately 6,000 school districts and will be the third CRDC to be conducted using the EDFacts Survey Tool. The session provided information about the sample, the data items that will be collected, timelines, and the collection tool. It also discussed steps that the Office for Civil Rights and the Office of Planning, Evaluation and Policy Development have taken to make the data from the 2006 CRDC, including full survey results, state and national projections, and a time series, available earlier and in ways that more effectively meet the needs of researchers and other data users. The session also provided an update on the activities of the recently established CRDC workgroup, which includes state and local representatives. The CRDC collects information about students in public schools, including enrollment, special education, discipline, promotion and graduation testing, high school completers, GED, and interscholastic athletics. Student data are disaggregated by race/ethnicity, sex, limited English proficiency and disability. Data are also collected on school characteristics, including grades offered, number of Advanced Placement courses offered, number of certified teachers and whether the school is a charter school, magnet school, alternative school, or serves only special education students.

Sessions in EDEN/EDFacts track:

I-C, II-C, III-C, IV-C, V-C, VI-C, VII-C, VIII-C, IX-C, X-C, XI-C, and XII-C

This session described the components of the Common Core of Data (CCD) dropout rate and averaged freshman graduation rate (AFGR). In addition to the rates themselves, the discussion described what constitutes a dropout and an on-time graduate, what enrollment base is used as denominator, and how we avoid disclosing students who dropout or fail to receive diplomas.

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Graduates and Dropouts on the CCD (233 KB)

Sessions in Federal track:

I-F, II-F, III-F, IV-D, IV-F, V-D, V-E, V-F, VI-D, VI-F, VII-D, VIII-E, X-E, and XI-E

In this paper, the presenter demonstrated the use of a data mining technique, decision tree model, to illustrate the various thought-processes of teachers who decide to stay in teaching. This graphical technique is used on NCES's B&B (93/03) dataset which provides a unique opportunity to combine personal characteristics of teachers with their teaching beliefs and perceptions; including classroom characteristics and practices. Decision tree models are easy to interpret, and they allow for logical representation of thought processes to understand particular behaviors (here, staying decisions of teachers). The findings have teacher retention implications at the national level.

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Seeing Is Believing: Using a Data Mining Technique on NCES' B&B Dataset to "See" What Motivates Public School Teachers to Stay in Teaching (123 KB)

A wealth of statistics—some going back 20 years—is easily accessible through the Common Core of Data's (CCD) online Build a Table and Locator applications. This session demonstrated how you can use these resources for fast and authoritative answers to common education questions.

Sessions in Federal track:

I-F, II-F, III-F, IV-D, IV-F, V-D, V-E, V-F, VI-D, VI-F, VII-D, VIII-E, X-E, and XI-E

This presentation was a discussion of how SIF enhances and changes the district business process and showed the data cleansing and cost savings that can be achieved on both the district and state level. A live demonstration of the data extraction and data cleansing process illustrated how data can be modified in real time for more accurate state and district reporting.

Sessions in SIF track:

II-G, III-G, IV-G, V-G, VI-G, VII-G, VIII-G, IX-G, X-G, XI-G, and XII-G

The complexity of Virginia's Discipline, Crime, and Violence (DCV) data collection was discussed as well as the enhancements that have improved the reporting process for the school districts in the state. The usage of DCV data has also improved with tools such as the Safe Schools Information Resource (SSIR) that provides transparency and allows schools, parents, and communities easy access to DCV data. A demonstration of the SSIR tool was presented.

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Complexity of Discipline, Crime, and Violence Data (44 KB)

The presenters described the National Adequate Yearly Progress (AYP) and Identification (NAYPI) database and discussed the potential uses of these data. Funded by the U.S. Department of Education, data were collected from state education agency officials and consolidated state performance reports, and the data were then put into a common standardized format enabling analyses across states and the nation. The database contains nearly 90,000 public schools in 15,000 districts across 50 states. The NAYPI database contains detailed information on whether each school met each of its 37 potential AYP targets including reading proficiency, math proficiency, reading test participation, math test participation, and the other academic indicator for the "all students" group and each of eight student subgroups in the years 2003–04, 2004–05, 2005–06 and three years of identification for improvement status under NCLB. AYP and identification data have been merged with the Common Core of Data (CCD) to provide a variety of demographic variables. To display how the data can be used to address policy issues of interest, we briefly presented analyses of the reasons that schools missed AYP, the percentage of students whose test scores were used in the accountability system, and the estimated number of schools affected by existing proposals for differentiated accountability. The session summarized the accomplishments and lessons learned over the last four years creating and analyzing the database. This overview also described upcoming releases and how the NAYPI can be merged with AYP data elements from EDFacts to conduct more complex longitudinal analyses.

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A National Adequate Yearly Progress and Identification Database (390 KB)