[Federal Register Volume 87, Number 83 (Friday, April 29, 2022)]
[Notices]
[Pages 25477-25479]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2022-09239]


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DEPARTMENT OF EDUCATION

[Docket ID ED-2022-IES-0051]


Request for Information on the Existence and Use of Large 
Datasets To Address Education Research Questions

AGENCY: Institute of Education Sciences, Department of Education.

ACTION: Request for information.

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SUMMARY: The National Center for Education Research (NCER), a center 
within the U.S. Department of Education's Institute of Education 
Sciences, funds and coordinates high-quality, innovative research that 
addresses the biggest challenges facing education in the 21st century. 
Through this request for information (RFI), NCER seeks public input to 
help us identify existing large datasets that may be useful for 
research and to understand the challenges and limitations that may 
affect access and their value for research.

DATES: We must receive your comments by May 31, 2022.

ADDRESSES: Comments must be submitted via the Federal eRulemaking 
Portal at regulations.gov. However, if you require an accommodation or 
cannot otherwise submit your comments via regulations.gov, please 
contact the program contact person

[[Page 25478]]

listed under FOR FURTHER INFORMATION CONTACT. The Department will not 
accept comments by fax or by email. To ensure that the Department does 
not receive duplicate copies, please submit your comments only once. 
Additionally, please include the Docket ID at the top of your comments.
    The Department strongly encourages you to submit any comments or 
attachments in Microsoft Word format. If you must submit a comment in 
Adobe Portable Document Format (PDF), the Department strongly 
encourages you to convert the PDF to ``print-to-PDF'' format, or to use 
some other commonly used searchable text format. Please do not submit 
the PDF in a scanned format. Using a print-to-PDF format allows the 
Department to electronically search and copy certain portions of your 
submissions to assist in the process.
    Federal eRulemaking Portal: Go to www.regulations.gov to submit 
your comments electronically. Information on using Regulations.gov, 
including instructions for accessing agency documents, submitting 
comments, and viewing the docket, is available on the site under the 
``FAQ'' tab.
    Privacy Note: The Department's policy for comments received from 
members of the public is to make these submissions available for public 
viewing in their entirety on the Federal eRulemaking Portal at 
www.regulations.gov. Therefore, commenters should be careful to include 
in their comments only information that they wish to make publicly 
available. We encourage, but do not require, that each respondent 
include their name, title, institution or affiliation, and the name, 
title, mailing and email addresses, and telephone number of a contact 
person for the institution or affiliation, if any.

FOR FURTHER INFORMATION CONTACT: Erin Higgins, Program Officer, 
National Center for Education Research, Institute of Education 
Sciences, U.S. Department of Education, 400 Maryland Avenue SW, 
Washington, DC 20202-7240. Telephone: (202) 706-8509. You may also 
email your questions to <a href="/cdn-cgi/l/email-protection#4c093e25226204252b2b25223f0c2928622b233a"><span class="__cf_email__" data-cfemail="0a4f7863642442636d6d6364794a6f6e246d657c">[email protected]</span></a>, but as described above, 
comments must be submitted via the Federal eRulemaking Portal at 
regulations.gov.
    If you are deaf, hard of hearing, or have a speech disability and 
wish to access telecommunications relay services, please dial 7-1-1.

SUPPLEMENTARY INFORMATION:

Background

    The number of large education-related datasets is growing, and we 
have new opportunities to leverage these data to address critical 
questions of policy and practice. For example, State longitudinal data 
systems (SLDS) can support research on the questions that State 
agencies have about a specific education issue, program, or policy. 
SLDSs have the potential to support lower-cost, faster research by 
avoiding the need for costly primary data collection. Similarly, 
education technologies generate large amounts of data that--after 
ensuring students' privacy is protected--can potentially provide 
valuable insights about learning. Despite the large amount of raw data 
collected by these technologies, there are legal, practical, and 
methodological barriers to conducting research that leverages these 
types of datasets to understand and improve students' education 
outcomes. Education researchers seeking to conduct studies using these 
datasets confront challenges related to the validity of data elements 
and the logistics of data access in ways that protect students' 
privacy, consistent with local, State, and Federal law. Researchers 
face significant barriers and costs to access these datasets, which 
leads to only a small number of education studies with large sample 
sizes, despite the known advantages of these types of studies.
    There are examples of the potential insights to be gained from 
these data, and the fields of educational data mining and learning 
analytics have developed methods and insights for working with large 
datasets. For example, researchers have analyzed data collected in the 
digital administration of NAEP, which has led to insights into multiple 
aspects of student test-taking strategies.<SUP>1 2</SUP>
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    \1\ Arslan, B., Gong., T., Feng, G., Agard, C., & Keehner, M. 
(2021, June 8). Going beyond scores: Understanding fourth-graders' 
scientific inquiry practices with process data. [Paper 
presentation]. The 2021 Virtual Annual Meeting of the National 
Council on Measurement in Education.
    \2\ Wang, N. & Circi, R. (2020, August). Revisiting Omit and 
Not-Reached Scoring Rule using NAEP Process Data. In J. Weeks 
(Chair). Diving into NAEP Process Data to Understand Students' Test 
Taking Behaviors. Symposium accepted to the meeting of the 2021 
National Council on Measurement in Education, Baltimore, MD.
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    Data privacy is central to the ethical conduct of research. Any 
plans to leverage the large amounts of data that are being collected 
through education technology, State longitudinal data systems, and 
other sources must be designed to minimize the risk of disclosure in 
order to protect the privacy of students.
    Through this RFI, we seek public comment to help us identify 
existing large datasets, especially those that are generated using 
education technology, that may be useful for research; identify best 
practices for creating new, large datasets that are valuable for 
research; understand the challenges and limitations that may impact 
data access; and develop and implement plans to protect students' 
privacy.
    This is a request for information only. This RFI is not a request 
for proposals (RFP) or a promise to issue an RFP or a notice inviting 
applications. This RFI does not commit the Department to contract for 
any supply or service whatsoever. Further, we are not seeking proposals 
and will not accept unsolicited proposals. The Department will not pay 
for any information or administrative costs that you may incur in 
responding to this RFI. The documents and information submitted in 
response to this RFI will not be returned.
    We will review every comment, and the comments in response to this 
RFI will be publicly available on the Federal eRulemaking Portal at 
www.regulations.gov. Please note that IES will not directly respond to 
comments.

Solicitation of Comments

    We invite stakeholders who are aware of large datasets relevant to 
education and learning, especially those generated through education 
technology; stakeholders who have perspectives on the value of these 
datasets for education research; and stakeholders who are aware of 
challenges and limitations to both access and use of large datasets to 
share responses to the following questions in their comments:
    (1) What public or restricted use education-related datasets are 
available for training students in data mining/machine learning 
methods? What training needs are not being met by the datasets that are 
currently available?
    (2) What open or restricted use education-related datasets are 
available to train new artificial intelligence models or to test 
hypotheses using data mining/machine learning methods? What research 
needs are not being met by the datasets that are currently available?
    (3) What work do researchers need to do to access, and then explore 
the quality of, an existing dataset before conducting research with it? 
What aspects of this work could be reduced or conducted just once so 
that future researchers can reduce the time needed to complete a 
research project?
    (4) How do researchers determine the validity of data elements 
within previously collected datasets? What

[[Page 25479]]

challenges are frequently encountered related to how those data align 
to constructs of interest?
    (5) What are promising approaches to testing and improving the 
validity of metrics within large datasets, especially those datasets 
that are developed through interactions with education technology?
    (6) How likely is it that existing datasets, especially those that 
come out of education technology, contain data that are valuable for 
researchers and of sufficient quality that research could be conducted 
with a high amount of rigor?
    (7) To what extent do existing datasets capture enough information 
to address research questions related to diversity, equity, inclusion, 
and accessibility? What additional data should be collected to address 
these questions?
    (8) What are the best practices for creating new datasets or 
linking existing datasets and sharing them with researchers (open or 
restricted use) while prioritizing the privacy of individuals and 
adhering to local, State, and Federal laws? What barriers and 
limitations exist?
    (9) What role can IES play in developing infrastructure that 
supports the use of large-scale datasets for education research?
    Accessible Format: By request to the program contact person listed 
under FOR FURTHER INFORMATION CONTACT, individuals with disabilities 
can obtain this document in an accessible format. The Department will 
provide the requestor with an accessible format that may include Rich 
Text Format (RTF) or text format (txt), a thumb drive, an MP3 file, 
braille, large print, audiotape, or compact disc, or other accessible 
format.
    Electronic Access to This Document: The official version of this 
document is the document published in the Federal Register. You may 
access the official edition of the Federal Register and the Code of 
Federal Regulations at www.govinfo.gov. At this site you can view this 
document, as well as all other documents of this Department published 
in the Federal Register, in text or Portable Document Format (PDF). To 
use PDF you must have Adobe Acrobat Reader, which is available free at 
the site.
    You may also access documents of the Department published in the 
Federal Register by using the article search feature at 
www.federalregister.gov. Specifically, through the advanced search 
feature at this site, you can limit your search to documents published 
by the Department.

Mark Schneider,
Director, Institute of Education Sciences.
[FR Doc. 2022-09239 Filed 4-28-22; 8:45 am]
BILLING CODE 4000-01-P


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