Notice2022-09239
Request for Information on the Existence and Use of Large Datasets To Address Education Research Questions
Primary source
Metadata and text below are from the Federal Register, a public-domain U.S. government work. Always verify the official published version before relying on it for any legal matter.
Published
April 29, 2022
Issuing agencies
Education Department
Abstract
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.
Full Text
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<title>Federal Register, Volume 87 Issue 83 (Friday, April 29, 2022)</title>
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[Federal Register Volume 87, Number 83 (Friday, April 29, 2022)]
[Notices]
[Pages 25477-25479]
From the Federal Register Online via the Government Publishing Office [<a href="http://www.gpo.gov">www.gpo.gov</a>]
[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 <a href="http://regulations.gov">regulations.gov</a>. However, if you require an accommodation or
cannot otherwise submit your comments via <a href="http://regulations.gov">regulations.gov</a>, please
contact the program contact person
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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 <a href="http://www.regulations.gov">www.regulations.gov</a> 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
<a href="http://www.regulations.gov">www.regulations.gov</a>. 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
<a href="http://regulations.gov">regulations.gov</a>.
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
<a href="http://www.regulations.gov">www.regulations.gov</a>. 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
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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 <a href="http://www.govinfo.gov">www.govinfo.gov</a>. 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
<a href="http://www.federalregister.gov">www.federalregister.gov</a>. 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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