R&D Hub

Modules on how to use the EdSurvey R package to analyze various NCES data have been added to NCES’s Distance Learning Dataset Training (DLDT) system. The DLDT is an interactive web tool that allows data users to learn about NCES data products and to appraise their usefulness for various purposes.

Challenge.gov, the official hub for prize challenges and competitions across the U.S. federal government, allows federal agencies to use prize competitions to incentivize public problem-solvers to identify innovative solutions for difficult issues.

Through its Focus on NAEP website, the Nation's Report Card provides non-technical audiences a closer look at how the NAEP assessment works. In turn, stakeholders outside of the research community can have a better understanding of why this assessment matters. In this week’s post we look at how researchers can benefit from exploring this useful tool.

As discussed in the “NAEP R&D 2021 Year in Retrospect,” the EdSurvey program made big strides in 2021, including joining the official NAEP Database Products and the NAEP Data Companion and releasing updated packages with data from NHES, SOCCS, TIMSS 2019, OECD PISA 2018 Financial Literacy, and 2017 US PIACC. In 2022 and beyond, the EdSurvey program will continue to enhance usability and accessibility of NCES and international large-scale assessment data.

The National Assessment of Educational Progress (NAEP) began to transition to digitally based assessments (DBAs) in 2017. DBAs enable the collection of data on students’ interactions with the assessment. However, these data are not trivial to analyze, given their time-dependent nature, abundance, structural complexity, and inevitable errors in collection. For the full potential of process data to be realized, researchers must possess analytical skills that go beyond traditional statistical analysis. In the upcoming 2022 NCME conference, researchers from American Institutes for Research (AIR) , under the guidance of the National Center for Education Statistics (NCES), will conduct a training session using methods taken from business process mining and graph theory to advance the use of NAEP process data.