The Learning Agency’s staff has been working with unreleased data, also known as “restricted data,” from the Institute for Education Sciences (IES), part of the US Department of Education, for decades. The datasets are a treasure trove for researchers and help understand the current state of education in the US as they contain extensive data on students and educators.
As an example, one of the members of our team looked at the background questions from the National Assessment of Education Progress assessment and found that 37 percent of fourth graders say their math work is often or always too easy and almost a third of middle schoolers report they read less than five pages a day at home or at school. Findings such as these can provide a catalyst for specific interventions and nationwide policy.
The NAEP or National Assessment of Education Progress is particularly important. It is known as the nation’s report card and it is likely the most well known assessment that IES administers. The NAEP is the largest nationally representative and continuing assessment of what America’s students know and can do in various subject areas. This assessment provides information on students, their parents, teachers, and principals across the US in K-12 and is considered the “gold standard” for what student achievement looks like in the US.
The NAEP is the largest nationally representative and continuing assessment of what America's students know and can do in various subject areas.
The focus of the NAEP is on student performance in core subjects. However, the agency also holds a wealth of data about school status and teacher experience. While these might not be considered the primary outcomes, this supplemental data can help researchers understand underlying problems that may be affecting students and are not apparent in the subject area testing.
For instance, information on schools and staffing, found in the Schools and Staffing Survey (SASS) pre-2011 and now the National Teacher and Principal Survey (NTPS), provide data on the context of elementary and secondary education, including teacher demand, teacher and principal characteristics, etc. These questionnaires can inform long-term understanding of learning and inform the next generation of educational policy by seeing the additional staff and educator factors and needs that are impacting students.
While aggregate analyses of data (such as the trends in math scores year over year) are extremely useful for understanding the policy implications and status of schools in the US, the student-level data collected by IES can greatly benefit students in the form of algorithm training for Artificial Intelligence (AI) due to it being extensive and collected on large numbers of students. AI tools for students, teachers, administrators, and even parents are a huge growing sector in education.
These tools are built by creating algorithms on large amounts of data that are representative of the population the tool seeks to help.
To teach an AI tool to help students write essays, the tool’s underlying algorithm must first see thousands of good and bad examples to learn how to recognize issues, target style, and provide informative feedback/output.
For instance, to teach an AI tool to help students write essays, the tool’s underlying algorithm must first see thousands of good and bad examples to learn how to recognize issues, target style, and provide informative feedback/output. The tools that can benefit from the immense amount of IES information are endless. To help researchers understand the extent to which data can be used for the public good, see this memo that we pulled together with ideas. In the memo, you will find descriptions of currently available data and ideas of how that can be refined into a tool to help the education field.
The memo we provided barely scratches the surface of the IES data available. IES maintains a repository of available restricted-use datasets that you can request access to. However, be aware that these datasets available are years behind the data collection. There is a considerable lag between when students take standardized tests or surveys and when the data becomes available, due to the rigorous review and safety practices at IES. Expect the most recent data to be a few years old. See this memo for examples of IES restricted-use datasets that would be beneficial as public information.
After determining what data is available and how that data can be used in research or tool development, the next step is seeking access. This can be a lengthy process. While the outcomes of work on education data are important, there is a strong need for safety and caution when working with young student data. As such, the process of obtaining data is rigorous, and navigating the process for restrictive data can be extremely daunting for many researchers.
Below are key things to know when working with IES data:
1. ADRF: The End of “Cold Rooms”
The first thing to know is that storing and accessing federal education data is no longer as restricted as it once was. The Administrative Data Research Facility (ADRF), established by the federal government, allows researchers to access government data without the need for the old “cold rooms”—secure rooms that lacked regular internet access and required additional security protocols. The ADRF is essentially a platform where confidential data can be securely housed and accessed remotely by researchers in the US.
ADRF offers more flexibility while maintaining the high-security standards necessary for handling sensitive information. For instance, multiple team members can access at once and can access from any location via the ADRF portal on the internet. This means research teams are not constrained by location and time, people from different universities and organizations can work together on the same project.
2. The Application is Collaborative
When submitting your application to access IES data, expect a back-and-forth with the IES team. While the application for restrictive use data asks very detailed questions, the IES team will likely come back with further questions about the information you provided that is specific to your work. The more detail you provide upfront, the faster and simpler this process will be to get to application approval.
While the application for restrictive use data asks very detailed questions, the IES team will likely come back with further questions about the information you provided that is specific to your work.
Overall, be aware that this is an iterative and collaborative process, where you’ll receive feedback and need to make edits to your application. Ensure that your project timeline accounts for this process, as each round of feedback could take additional time.
3. Don’t Forget Physical Paperwork
Although much of the application process is conducted online, certain aspects such as non-disclosure agreements and security agreements still require physical paperwork. This paperwork needs to be signed or notarized in person and sent via mail. The IES documents will have specific instructions detailed on them so read carefully.
We also recommend scanning all documents once they are complete and using tracking options through your postal service to monitor arrival. This helps maintain smoother communication with the IES team and ensures your paperwork reaches them safely.
4. Be Specific with Your Dataset Requests
One common mistake when applying is not being specific enough when requesting a dataset. Many datasets have similar names or may have been collected in the same year, so it’s crucial that you reference the correct title as it appears in the repository. This can save time and ensure you are granted access to the data you actually need.
If you are not detailed enough on your data request, the IES team will follow up with you and ask for clarification which will delay your approval process. Reach out to the IES team via email at IESData.Security@ed.gov if you have any questions or confusion.
5. Mind the Export Restrictions
Given the sensitive nature of the data IES offers, there are export restrictions to keep in mind. Anything you want to export or share externally will go through a manual review by IES to obtain approval.
Plan ahead when budgeting time for export reviews, especially if you intend to publish findings based on this data. Additionally, think carefully about how working in a secure, restricted environment will affect collaboration. Since certain practices like screen sharing and screenshots are prohibited, plan alternative methods of team collaboration (especially if you are collaborating with people in other locations) that comply with security protocols, such as simultaneously being in the server to view any analyses or results.
The Final Takeaway
Navigating the IES data access process may seem daunting, but with the right preparation and attention to detail, it can lead to insightful, impactful research that makes a difference in the world of education policy.
