The restricted-use datasets for the National Assessment of Educational Progress (NAEP) High School Transcript Study (HSTS) 2019 include ASCII-formatted data files, record layouts, SAS- and SPSS-formatted databases, codebooks, and SAS and SPSS programs. The NAEP High School Transcript Study analyzes transcripts from a national sample of U.S. public and private school graduates who also took the 2019 NAEP 12th grade assessments in mathematics and science. The study provides valuable information about coursetaking patterns disaggregated by demographic characteristics and the relationship between NAEP scale scores and various graduate characteristics.

Explore the NAEP HSTS 2019 User’s Guide and Technical Report (NCES 2023077).

The 2019 NAEP High School Transcript Study (HSTS) describes the coursetaking patterns and academic performance of graduates from a national sample of U.S. public and private schools who also took the 2019 NAEP twelfth-grade mathematics and science assessments. This report uses data from the 1990, 2000, 2009, and 2019 NAEP HSTS for coursetaking results and from 2005, 2009, and 2019 for comparisons to NAEP.

The study of high school graduates’ academic performance and coursetaking patterns is based on an analysis of their transcripts and NAEP twelfth-grade mathematics and science assessment results. HSTS show trends from 1990, 2000, 2009, and 2019 in grade point averages, course credits earned, curriculum levels, and various coursetaking patterns. The 2019 HSTS uses a new course classification system, the School Courses for the Exchange of Data (SCED), to provide a more detailed breakdown of cross-disciplinary coursetaking programs such as Career and Technical Education (CTE) and Science Technology Engineering and Mathematics (STEM) coursetaking.

The study also compares graduates’ average NAEP scale scores from the twelfth-grade mathematics and science assessments to the academic achievement reported in their transcripts. The linkage of the NAEP twelfth-grade mathematics and science assessments to HSTS provides the opportunity for school leaders, policy makers, and researchers to analyze student performance by a rich set of HSTS and NAEP contextual factors.

This report provides information for administrators in local education agencies who are considering early warning systems to identify at-risk students. Districts use early warning systems to target resources to the most at-risk students and intervene before students drop out. Schools want to ensure the early warning system accurately identifies the students that need support to make the best use of available resources. The report compares the accuracy of using simple flags based on prior academic problems in school (prior performance early warning system) to an algorithm using a range of in- and out-of-school data to estimate the specific risk of each academic problem for each student in each quarter. Schools can use one or more risk-score cutoffs from the algorithm to create low- and high-risk groups. This study compares a prior performance early warning system to two risk-score cutoff options: a cutoff that identifies the same percentage of students as the prior performance early warning system, and a cutoff that identifies the 10 percent of students most at risk.

The study finds that the prior performance early warning system and the algorithm using the same-percentage risk score cutoffs are similarly accurate. Both approaches successfully identify most of the students who ultimately are chronically absent, have a low grade point average, or fail a course. In contrast, the algorithm with 10-percent cutoffs is good at targeting the students who are most likely to experience an academic problem; this approach has the advantage in predicting suspensions, which are rarer and harder to predict than the other outcomes. Both the prior performance flags and the algorithm are less accurate when predicting outcomes for students who are Black.

The findings suggest clear tradeoffs between the options. The prior performance early warning system is just as accurate as the algorithm for some purposes and is cheaper and easier to set up, but it does not provide fine-grained information that could be used to identify the students who are at greatest risk. The algorithm can distinguish degrees of risk among students, enabling a district to set cutoffs that vary depending on the prevalence of different outcomes, the harms of over-identifying versus under-identifying students at risk, and the resources available to support interventions.

The PISA 2018 Public Use File (PUF) consists of data from the PISA 2018 sample. Statistical confidentiality treatments were applied due to confidentiality concerns. The PUF can be accessed from the National Center for Education Statistics website at http://nces.ed.gov/surveys/pisa/datafiles.asp.

For more details on the data, please refer to chapter 9 of the PISA 2018 Technical Report and User Guide (NCES 2021-011).

The PISA YAFS 2016 Public Use File (PUF) consists of data from the PISA YAFS 2016 sample. PISA YAFS was conducted in the United States in 2016 with a sample of young adults (at age 19) who participated in PISA 2012 when they were in high school (at age 15). In PISA YAFS, students took the Education and Skills Online (ESO) literacy and numeracy assessments, which are based on the Program for the International Assessment of Adult Competencies (PIAAC). It contains data for individuals including responses to the background questionnaire and the cognitive assessment. Statistical confidentiality treatments were applied due to confidentiality concerns.

For more details on the data, please refer to chapter 8 of the PISA YAFS 2016 Technical Report and User Guide (NCES 2021-020).

The PISA 2018 Restricted Use File (RUF) consists of restricted-use data from PISA 2018 for the United States. The data file and documentation includes the data file, a codebook, instructions on how to merge with the U.S. PISA 2018 public-use dataset (NCES 2021-047), and a cross-walk to assist in merging with other public datasets, such as the Common Core of Data (CCD) and Private School Survey (PSS). As these data files can be used to identify respondent schools, a restricted-use license must be obtained before access to the data is granted. Click on the restricted-use license link below for more details https://nces.ed.gov/surveys/pisa/datafiles.asp.

For more details on the data, please refer to chapter 9 of the PISA 2018 Technical Report and User Guide (NCES 2021-011).