Laura Bornfreund
Senior Fellow, Early & Elementary Education
Data can be a valuable tool in helping states and school districts implement effective policies and practices and teachers shape their instruction to meet the individual needs of their students. But how those data are used is just as important as the kind of information collected.
States are making progress toward building longitudinal data systems and implementing what the Data Quality Campaign (DQC) calls the 10 Essential Elements. For example, states have collected data on students’ standardized assessment scores and graduation status (for the list of the full 10, see the table below).
According to DQC’s latest survey of states, however, most continue to struggle using their data systems to inform and guide policy decisions.
Thirty-eight states, for example, have not established policies around sharing data across agencies. And while 40 states provide access to student-level longitudinal data to principals and 28 to teachers, only 10 states have policies requiring data literacy for teacher and principal certification.
It’s great that teachers and principals have access to student data in so many states. But if they have not received training on how to use it most effectively, it’s unlikely that the data can inform school-based program decisions or instruction.
Below is how states say they are progressing on implementing important data system elements, according to the DQC survey:
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State Progress on Implementing 10 Essential Elements (including DC and Puerto Rico) |
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Element |
States |
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Statewide student identifier |
All |
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Student level enrollment data |
All |
|
Student level test data |
All |
|
Information on untested students |
All except DC |
|
Statewide Teacher Identifier with a Teacher-Student Match |
All except Montana, South Dakota, Colorado, Vermont, Connecticut, New Jersey, DC and Alaska |
|
Student-Level Course Completion (Transcript) Data |
All except Montana, Colorado, Arizona, Alaska, Oklahoma, Maine, Vermont, Connecticut, Pennsylvania, New Jersey and Rhode Island |
|
Student-Level SAT, ACT, and Advanced Placement Exam Data |
All except Montana and Puerto Rico |
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Student-Level Graduation and Dropout Data |
All |
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Ability to Match Student-Level P-12 and Higher Education Data |
All except Ohio, South Carolina and Puerto Rico |
|
A state data audit system |
All |
When it comes to incorporating and effectively using data on children not yet in kindergarten – including the programs they attend and the educators who teach and care for them – there is still plenty of work to do. According to DQC, 36 states can match and share data between K-12 and pre-kindergarten programs. But what kind of data is an important question. Last year, the Early Childhood Data Collaborative (an effort spearheaded by DQC) reported that while many states collect quite a bit of data from some early childhood programs, few states collect data from all early childhood programs (such as child care centers, Head Start and preschools not affiliated with the public schools). We wrote about states’ challenges in our report Many Missing Pieces.
Below is a snapshot from the DQC’s report on which states say they are using data most effectively:
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State Progress on Implementing 10 State Actions (including DC and Puerto Rico) |
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Action |
States |
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Link state K–12 data systems with early learning, postsecondary education, workforce, social services and other critical agencies |
AK, AR, DE, FL, MD, MO, NC, RI, TX, UT, WA |
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Create stable, sustained support for robust state longitudinal data systems. |
AZ, AR, CO, CT, DE, DC, FL, GA, ID, IL, KS, LA, MA, MI, NE, NJ, NY, ND, OH, OR, RI, TN, TX, VA, WA, WV, |
|
Develop governance structures to guide data collection, sharing and use |
AZ, AK, CA, CT, DE, DC, GA, HI, ID, IL, IN, IA, KY, ME, MD, MA, MI, MN, MS, MO, NE, NM, NC, ND, OH, OR, PA, RI, SD, TN, TX, UT, VA, WA, WV, WI |
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Build state data repositories (e.g., data warehouses) that integrate student, staff, financial and facility data |
All except AL, DC, IL, MT, ND, OK, SD, WA, |
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Implement systems to provide all stakeholders with timely access to the information they need while protecting student privacy |
AR, NH |
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Create progress reports with individual student data that educators, parents and students can use to improve student performance |
AL, AR, CO, CT, DE, DC, FL, GA, HI, ID, LA, ME, MI, MN, MO, MT, NV, NH, NJ, OH, OR, PA, TN, TX, UT, VA, WV, WI |
|
Create reports that include longitudinal statistics on school systems and groups of students to guide school-, district-, and state-level improvement efforts |
AL, AK, AR, CA, CO, CT, DE, DC, FL, GA, HI, IL, IN, KS, LA, ME, MD, MA, MI, MN, MO, NV, NH, NM, NC, OH, OR, PA, RI, SC, TN, TX, UT, VA, WA, WI |
|
Develop a purposeful research agenda and collaborate with universities, researchers and intermediary groups to explore the data for useful information |
AL, AK, AR, CO, CT, DE, FL, GA, HI, IL, KS, LA, ME, MA, MI, MN, MO, NV, NH, NM, NC, OH, RI, SC, TN, TX, UT, VA, WA, WV, WI |
|
Implement policies and promote practices, including professional development and credentialing, to ensure educators know how to access, analyze and use data appropriately |
FL, NC, SC |
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Promote strategies to raise awareness of available data and ensure that all key stakeholders, including state policymakers, know how to access, analyze and use the information |
AL, AR, CA, CO, DE, FL, GA, KS, LA, ME, MI, MO, MT, NV, NH, NJ, NC, OH, RI, TN, TX, UT, WI |
The states that appear to be making the most progress on these actions are Arkansas, Florida and Texas.
In the report, DQC discusses a few “speed bumps” slowing other states’ progress: turf, trust, technical issues and time. Turf refers to education being full of silos, especially in early education, which leads to state agencies not being comfortable with sharing or able to share data. Some education stakeholders simply don’t trust the quality of data and how it’s used. Technical issues are also a problem for many states, though solutions are emerging. And most states are contending with scarce resources and competing priorities – a tough environment for building and effectively using longitudinal data systems that take both time and money.
We hope states continue to make improving their data systems a priority as well as begin to make additional investments in how the data are used to inform policy decisions, practice and instruction. Data on the types of early learning experiences young children have before they enter school, for example, could make it much easier for kindergarten teachers to prepare for the cognitive and developmental needs of their incoming students. Having the ability to answer questions such as “what are the features of effective early learning programs” or “which children have access to high-quality programs” would be helpful to policymakers making difficult resource decisions required during tough economic times like those that many states are facing now.