Teachers' Use of Student Data Systems to Improve Instruction: 2005 to 2007


The collection, analysis, and use of education data are central to the improvement of student outcomes envisioned by No Child Left Behind (NCLB). Over the past six years, meeting the data requirements of NCLB and adapting or acquiring electronic data systems capable of generating the required student data reports have consumed much of the attention of district and state assessment and technology offices. The assumption of current policymakers is that the use of data from student data systems will lead to positive impacts on instruction and student achievement. But an examination of current practice suggests that the use of electronic student data systems and instructional decision-making are not fully integrated. Data-informed decision-making goes beyond the use of an electronic data system; it includes the adoption of a continuous improvement strategy that includes a set of expectations and practices for the ongoing examination of student data to ascertain the effectiveness of educational activities and, subsequently, to refine programs and practices to improve outcomes for students. If data are to influence the quality of the instruction that students receive, teachers who work with students day-to-day need access to timely information relevant to instructional decisions and the skills necessary to make sense of student data reports. Many district and school leaders are working to inspire and support teachers' involvement in data-informed decision-making. Their efforts, combined with supportive education policies and improved data systems, are aimed at promoting data use practices at the school and classroom levels.

The current brief is the second in a two-part series examining teachers' access to and use of data from student data systems. The first brief indicated that about half of all teachers (48 percent) reported having access to a student data system in 2004-05, but teachers did not necessarily have appropriate data or tools they needed to make good use of student data in planning and individualizing instruction. [  1  ]

What is data-informed educational decision-making?

In an education context, data-informed decision-making is the analysis and use of student data and information concerning education resources and processes to inform planning, resource allocation, student placement, and curriculum and instruction. The practice entails regular data collection and ongoing implementation of an improvement process.

What is a student data system?

An electronic information system to assist in the organization and management of student data. Data systems consist of hardware and software that provide many different functions to users, such as storing current and historical data, rapidly organizing and analyzing data, and developing presentation formats or reporting interfaces.

Key Findings

The following findings are based on analyses of national survey data from district technology coordinators and teachers from 2005 and 2007:

  • There was a significant increase in teacher-reported access to electronic student data systems between 2005 and 2007—from 48 percent to 74 percent.
  • Even so, teachers are more likely to report having electronic access to students' grades and attendance than to achievement data: Only 37 percent of all teachers reported having electronic access to achievement data for the students in their classrooms in 2007.
  • Teachers express a desire for more professional development around the use of data, and those teachers who do feel better-than-average support from their colleagues and schools for working with data are more likely to use student data for instructional purposes.

Purpose of the Brief

Using data from national surveys of teachers and school districts, this brief documents the results of efforts to promote data-informed decision-making within schools. Estimates of the prevalence of K-12 teachers' access to and use of electronic student data systems at two time points (school years 2004-05 and 2006-07) are provided. Specifically, the brief addresses three research questions:

  • How broadly are student data systems being implemented in districts and schools?
  • How prevalent are supports for data use and tools for generating and acting on data?
  • How are school staff using student data systems?

Data Sources

This brief reports on analyses of survey data from the U.S. Department of Education's National Educational Technology Trends Study (NETTS), which examines the implementation of the Enhancing Education Through Technology Program as authorized by under the No Child Left Behind Act of 2001. The primary data used in this brief consist of survey responses from:

  • 1,028 district technology directors surveyed during spring 2005 and spring 2007, and
  • 6,017 teachers surveyed during fall 2005 and 1,779 teachers surveyed in spring 2007.

The teachers were clustered in schools sampled from the districts participating in a NETTS district survey. [  2  ] Both district and teacher respondents were asked to report on activities during the 2003-04 and 2006-07 school years.

Teachers were sampled from 975 schools within the districts selected for the NETTS district survey. [  3  ] Higher-poverty [  4  ] schools were oversampled to obtain more precise data about their technology use. Response rates were 94 percent and 99 percent for the district surveys in 2007 and 2005, respectively, and 85 percent and 82 percent for the teacher surveys in the same years. Sampling weights were applied to the teacher data to obtain nationally representative estimates.

The survey data are part of a larger study that is documenting the availability of electronic student data systems, their characteristics, and the prevalence and nature of data-informed decision-making in districts and schools. Case study findings from this larger data collection effort that have yet to be published are used to help interpret survey results.

List of Exhibits

Exhibit 1 Types of Student Data Stored Electronically, 2004-05 and 2006-2007
Exhibit 2 Types of Student Status Data That Districts Stored Electronically in 2006-07
Exhibit 3 Types of Student Achievement Data That Districts Stored Electronically in 2006-07
Exhibit 4 Percentage of Districts Granting Teachers Access to Their Students' Data, by Degree of Access in 2006-07
Exhibit 5 Teacher-reported Access to Data, by District-reported Categories of Teacher Access to Data in 2006-07
Exhibit 6 Percentage of Teachers Reporting Access to a Student Data System, by school Level and Survey Year: 2005 and 2007
Exhibit 7 Percentage of Teachers Reporting Access to a Student Data System, by School Poverty in 2007
Exhibit 8 Teacher-reported Categories of Data and Tools Available to Them: 2005 and 2007
Exhibit 9 Percentage of Teachers Who Reported Using a Student Data System at Least a Few Times a Year for a Specific Function: 2005 and 2007
Exhibit 10 Percentage of Teachers Who Reported Using an Electronic Student Data System at Least a Few Times a Year for a Specific Function, by Teaching Area and Year: 2005 and 2007
Exhibit 11 Teachers Indicating Support for Using Student Data to Guide Instruction, by Source of Support: 2005 and 2007
Exhibit 12 District Support for Professional Development in Data-informed Decision-making in 2006-07
Exhibit 13 Teacher Reflections on Personal Confidence and Institutional Support for Using Electronic Student Data Systems in 2006-07
Exhibit 14 Percentage of Teachers Using an Electronic Student Data System at Least a Few Times a Year for a Specific Function, by Level of Confidence and Support in 2006-07
Exhibit 15 Teacher Perceptions of Potential Benefit From Different Forms of Professional Development in 2006-07
Exhibit 16 Teacher Perceptions of Potential Benefit From Different Forms of Professional Development, by Personal Confidence in Using an Electronic Student Data System in 2006-07
Exhibit 17 Teacher Perceptions of Potential Benefit From Different Forms of Professional Development, by School AYP Status in 2006-07


  1. The first brief, Teachers' Use of Student Data Systems to Improve Instruction, is available at http://www.ed.gov/rschstat/eval/tech/teachers-data-use/
    (last accessed June 26, 2008). [ return to text ]

  2. The NETTS district sample of 1,039 districts was nationally representative with respect to poverty status, student enrollments, and location (urban or rural status). The 60 largest urban school districts across the country were selected with certainty (i.e. included in the sample from the outset). Districts composed entirely of special education schools and vocational-technical schools, as well as independent charter schools that are their own districts, were excluded from the district sampling frame because of their dissimilarity to "typical" districts. To obtain the NETTS teacher survey sample, schools were selected from the district survey sample. Teachers were selected from the sampled schools (schools were stratified by poverty and grade level). Although the sampling process was sequential, entities at each level were selected at random (i.e., teachers were randomly selected from staff rosters from each of the schools in the sample). [ return to text ]

  3. To be eligible for the teacher samples in 2007 and 2005, a teacher had to be teaching at the same school in the school year prior to survey administration (i.e., teachers new to the school were excluded). Teachers who did not teach core academic subjects also were omitted from the sample. The final teacher sample in 2007 consisted of 1,779 teachers from 865 schools. The larger sample of 6,017 teachers in 2005 was designed to provide robust, school-level estimates of technology use. [ return to text ]

  4. For schools, "higher poverty" was defined as above a specified cutoff in terms of the percentage of students who were eligible for the free or reduced-price lunch program. The dividing line between higher-poverty and lower-poverty schools was selected to ensure that for each school type (elementary, middle, or high school), there would be the same number of teachers in the higher-poverty and the lower-poverty groups, as reported in the National Center for Education Statistics Common Core of Data (CCD). Elementary schools with 29.7 percent of their students eligible for free or reduced-price lunches were classified as higher poverty. For middle and high schools, the poverty thresholds were 24.3 percent and 15.9 percent, respectively. [ return to text ]

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Last Modified: 10/06/2008