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National Center for Education Statistics

Public State and Local Education Job Openings, Hires, and Separations for February 2024

The National Center for Education Statistics recognizes the need to provide expanded economic data on education, including data about the education labor market. In this blog we will be presenting public education sector data from the February 2024 release of the Job Openings and Labor Turnover Survey (JOLTS) data produced by the Bureau of Labor Statistics. JOLTS data provide national monthly estimates of job openings, hires, and separations. These data can be used to monitor current labor market demand in education and to assess the presence or extent of labor shortages1.


JOLTS Design

JOLTS is a monthly survey of about 21,000 public and private employers across all nonagricultural industries in the 50 States and District of Columbia. JOLTS estimates are produced by industry sector, including education2. Additionally, JOLTS provides separate estimates for public and private education. This enables our analysis to focus on the public state and local education industry (“state and local government education” as referred to by JOLTS)3, which includes all persons employed by public elementary and secondary school systems and public postsecondary institutions.

The JOLTS program does not produce estimates by Standard Occupational Classification4When reviewing these findings, please note occupationswithin the public state and local education industry vary6 (e.g., teachers and instructional aides, administrators, cafeteria workers, and transportation workers).

 

Analysis

This analysis of JOLTS data highlights key statistics describing employment availability, hiring, and turnover in public local and state education. Table 1 includes estimates on the number of job openings, hires, and separations from February 2020 through February 2024. Table 2 includes estimates on the corresponding rates of job openings, hires, separations, fill and churn rate measures from February 2020 through February 2024. The job openings rate is computed by dividing the number of job openings by the sum of employment and job openings. Metric rates for hires, total separations, quits, layoffs and discharges, and other separations are defined by taking the number of each metric and dividing it by employment. Fill rate and churn rates are calculated economic measures that are not readily available from the JOLTS database. Fill rate is defined as the ratio of the number of hires to the number of job openings and the churn rate is defined as the sum of the rate of hires and the rate of total separations7,8.

 

Table 1. Number of job openings, hires, separations, and net change in employment in public state and local education, in thousands: February 2020 through February 2024

Employment activity

February 2020

February 2021

February 2022

February 2023

February 2024

Job openings

259

170*

322*

314*

226

Hires

141*

95

135

129

107

Total separations

74

77

106

81

80

   Quits

49

54

75*

54

56

   Layoffs and discharges

16

12

18

18

17

   Other separations

9

12

13

9

7

Net change in employment

67*

18

29

48

27

*Significantly different from February 2024 (p < .05).

NOTE: Data are not seasonally adjusted. Detail may not sum to totals because of rounding.

SOURCE: U.S. Department of Labor, Bureau of Labor Statistics, Job Openings and Labor Turnover Survey (JOLTS), 2020–2024, based on data downloaded April 2, 2024, from https://data.bls.gov/cgi-bin/dsrv?jt.

 

Table 2. Rate of job openings, hires, and separations in public state and local education and fill and churn rates: February 2020 through February 2024

Employment activity

February 2020

February 2021

February 2022

February 2023

February 2024

Job openings

2.3

1.6*

3.0*

2.8*

2.0

Hires

1.3*

0.9

1.3*

1.2

1.0

Total separations

0.7

0.8

1.0*

0.7

0.7

   Quits

0.4

0.5

0.7*

0.5

0.5

   Layoffs and discharges

0.1

0.1

0.2

0.2

0.2

   Other separations

0.1

0.1

0.1

0.1

0.1

Fill Rate

0.5

0.6

0.4

0.4

0.5

Churn Rate

2.0

1.7

2.3*

1.9

1.7

*Significantly different from February 2024 (p < .05).

NOTE: Data are not seasonally adjusted. Detail may not sum to totals because of rounding.

SOURCE: U.S. Department of Labor, Bureau of Labor Statistics, Job Openings and Labor Turnover Survey (JOLTS), 2020–2024, based on data downloaded April 2, 2024, from https://data.bls.gov/cgi-bin/dsrv?jt.

 

Overview of February 2024 Estimates

The number of job openings in public state and local education was 226,000 on the last business day of February 2024, which was higher than in February 2021 (170,000) and lower than in February 2022 (322,000) and February 2023 (314,000) (Table 1). In percentage rate terms, 2.0 percent of jobs had openings in February 2024, which was lower than in February of the previous two years (3.0 percent in 2022 and 2.8 percent in 2023) (Table 2). The number and percentage of job openings in February 2024 were not measurably different from the number and percentage in February 2020. The number of hires in public state and local education was 107,000 for February 2024, which was not measurably different from in February of the previous three years, but was lower than February 2020 (141,000) (Table 1). The number of job openings at the end of February 2024 (226,000) was nearly double the number of staff hired that month (107,000). In addition, the fill rate for that month (0.5) was less than 1, which suggests a need for public state and local government education employees that was not being filled completely by February 2024.

The number of total separations in the state and local government education industry in February 2024 (80,000) was not measurably different from in February of the previous four years. In February 2024, the number of quits (56,000) was higher than the number of layoffs and discharges (17,000). Layoffs and discharges accounted for 21 percent of total separations in February 2024 (which as not measurably different from the percentage of layoffs and discharges out of total separations in February 2023, 2022, 2021, or 2020) while quits accounted for 70 percent of total separations (which was not measurably different from the percentage of quits out of total separations in February 2023, 2022, 2021, or 2010).

This blog is part of NCES’ effort to share more economic data from other federal statistical agencies that is relevant to education. We plan to provide regular updates to selected months from JOLTS to enable our data users to find and follow useful information about the education workforce.

By Josue DeLaRosa, NCES


1 “Job Openings and Labor Turnover Survey Overview Page.” BLS.gov.  Last modified November 28, 2022. https://www.bls.gov/jlt/jltover.htm

2  For more information about these estimates, please see https://www.bls.gov/news.release/jolts.tn.htm.

3 JOLTS refers to this industry as state and local government education, which is designated as ID 92.

4 For more information on the reliability of JOLTS estimates, please see https://www.bls.gov/jlt/jltreliability.htm.

5 North American Industry Classification System (NAICS) is a system for classifying establishments (individual business locations) by type of economic activity. The Standard Occupational Classification (SOC) classifies all occupations for which work is performed for pay or profit. To learn more on the differences between NAICS and SOC, please see https://www.census.gov/topics/employment/industry-occupation/about/faq.html.

6 JOLTS data are establishment-based and there is no distinction between occupations within an industry. If a teacher and a school nurse were hired by an establishment coded as state and local government education, both would fall under that industry. (Email communication from JOLTS staff, April 7, 2023)

7 Skopovi, S., Calhoun, P., and Akinyooye, L. “Job Openings and Labor Turnover Trends for States in 2020.” Beyond the Numbers: Employment & Unemployment, 10(14). Retrieved on March 28, 2023, from https://www.bls.gov/opub/btn/volume-10/jolts-2020-state-estimates.htm.

8 Standard error estimates for fill rates, churn rates, and net employment were calculated using error propagation. The formulas used in deriving the standard errors for these estimates can be found in Taylor, J.R. (2022) “Propagation of Uncertainties,” in An introduction to error analysis: The study of uncertainties in physical measurements. New York: University Science Books, pp. 45–91.

Education Across America: Exploring the Education Landscape in Distant and Remote Rural Areas

In Education Across America, we explore the condition of education across four main geographic locales: cities, suburbs, towns, and rural areas. In this blog post, we use select findings from Education Across America to focus on the experiences of elementary and secondary school students in distant and remote rural areas (find the definitions of these locales and sublocales).

This blog post provides a snapshot of these students’ experiences and includes data—which were collected at various points during the 2019–20 school year—on family characteristics, characteristics of student populations, characteristics of schools, school choice, coursetaking, and educational outcomes.


Family Characteristics

The percentage of children ages 5 to 17 who were living in poverty in remote rural areas was higher than the national average. Similarly, a higher percentage of students in remote rural areas lived in homes without internet access compared with all other sublocales.

  • In 2019, the percentage of related children1 ages 5 to 17 who were living in poverty was 21 percent in remote rural areas, which was higher than the national average of 16 percent.
  • In 2019, among the 43 states for which data were available, the percentages of children in remote rural areas living in poverty ranged from 6 percent in Vermont to 42 percent in Arizona. The states with the highest percentages of children in poverty in remote rural areas were concentrated in the West (e.g., Arizona, New Mexico) and the South (e.g., South Carolina, Georgia).
  • In 2019, the percentage of students who lived in homes without internet access or with access only through dial-up was higher in remote rural areas (11 percent) than in all other sublocales (ranging from 3 percent in large suburban areas to 9 percent in distant rural areas).
  • In 2019, the percentage of students who had fixed broadband internet access2 was lower in remote rural areas (69 percent) than in in all other sublocales except distant rural areas (ranging from 77 percent in remote towns to 88 percent in large suburban areas).

Explore more data on Children in Rural Areas and Their Family Characteristics and Rural Students’ Access to the Internet.


Characteristics of Student Populations

Public schools in remote and distant rural areas had smaller populations of Black, Hispanic, and English learner students compared with those in other sublocales. However, public schools in remote rural areas had a larger populations of students with disabilities.

  • In fall 2019, the percentage of public school students who were Black was lower in remote (6 percent) and distant (7 percent) rural areas than in all other sublocales (ranging from 7 percent in fringe towns to 24 percent each in large and midsize cities).3
  • In fall 2019, the percentage of public school students who were Hispanic was lower in distant and remote rural areas (each 10 percent) than in all other locales (ranging from 19 percent in fringe rural areas to 43 percent in large cities).
  • In fall 2019, the percentage of public school students identified as English learners (EL) was lower for school districts in distant and remote rural areas (3 and 4 percent, respectively) than for school districts in all other sublocales (ranging from 5 percent in fringe rural areas to 17 percent in large cities).
  • In fall 2019, the percentage of public school students who were students with disabilities was higher for school districts in remote rural areas (16 percent) than for districts in all other sublocales, which ranged from 13 percent in midsized cities to 15 percent each in fringe and distant rural areas, all three town sublocales, and midsized suburban areas.

Explore more data on Children in Rural Areas and Their Family Characteristics and English Learners and Students with Disabilities in Rural Public Schools.


Characteristics of Schools

When compared with public schools in other sublocales, public schools in distant and remote rural areas had smaller school enrollment sizes and lower ratios of students to staff and teachers—meaning the average staff member or teacher was responsible for fewer students.

  • In fall 2019, a lower percentage of public schools were located in remote rural areas than in other types of rural areas. Six percent of all public schools were located in remote rural areas, 10 percent were located in distant rural areas, and 11 percent were located in fringe rural areas. In comparison, 26 percent were located in large suburban areas and 15 percent were located in large cities.
  • In fall 2019, average public school enrollment sizes in distant rural areas (285 students) and remote rural areas (165 students) were smaller than those of all other sublocales (ranging from 402 students in schools in remote towns to 671 students in schools in large suburban areas).
  • In fall 2019, the average public school pupil/teacher ratios and pupil/staff ratios in distant rural areas and remote rural areas were lower than the ratios in all other sublocales.
    • For example, the average pupil/teacher ratios in distant rural areas (14.0) and remote rural areas (12.5) were lower than the ratios in all other sublocales (ranging from 15.4 to 16.9).

Explore more data on Enrollment and School Choice in Rural Areas and Staff in Rural Public Elementary and Secondary School Systems


School Choice

Enrollment in both charter schools and private schools was lower in remote rural areas than in larger towns and cities, reflecting limited access to alternative educational institutions in remote rural areas.

  • In fall 2019, the percentage of public school students enrolled in charter schools was lower in remote rural areas (2 percent) than in all other sublocales, which ranged from 2 percent each in distant towns and distant rural areas to 17 percent in large cities.4
  • In fall 2019, the percentage of students enrolled in private schools was lower in remote rural areas (3 percent) than in the other sublocales, which ranged from 5 percent in distant rural areas and fringe towns to 14 percent in large cities.

Explore more data on Enrollment and School Choice in Rural Areas.


High School Coursetaking

Compared with those from cities, a lower percentage of public and private high school graduates from remote rural areas had taken advanced math but a higher percentage had taken career and technical education (CTE) courses.

  • In 2019, the percentage of graduates in remote rural areas who had earned any advanced mathematics credits was lower than the percentage in large cities (85 vs. 93 percent).
  • In 2019, the percentage of graduates who had completed any CTE course was higher in remote rural areas (97 percent) than in most other sublocales (ranging from 75 percent in large cities to 92 percent in fringe towns).5
  • In 2019, a higher percentage of graduates in remote rural areas than in most other sublocales had taken courses in the following six CTE subject areas: agriculture, food, and natural resources; architecture and construction; human services; information technology; manufacturing; and transportation, distribution, and logistics.
    • For example, 47 percent of graduates in remote rural areas had taken a course in agriculture, food, and natural resources, while this percentage ranged from 3 percent for graduates in large cities to 24 percent in distant towns.
  • Conversely, the percentage of graduates who had taken a course in engineering and technology was lower for those in remote rural areas (5 percent) than for those in most other sublocales (ranging from 12 to 16 percent).

Explore more data on College Preparatory Coursework in Rural High Schools and Career and Technical Education Programs in Rural High Schools.


Educational Outcomes

Public high school graduation rates were higher in remote rural areas than in cities. Despite this relatively high graduation rate, the percentage of adults age 25 and over with at least a bachelor's degree in remote rural areas was lower than in all other sublocales.  

  • In 2019–20, the adjusted cohort graduation rate (ACGR) in remote rural areas (88 percent) was higher than the ACGRs in cities (ranging from 79 percent in large cities to 86 percent in small cities) and in remote towns (85 percent) but lower than the ACGRs in large and midsized suburban areas (89 percent each) and in fringe and distant rural areas (91 and 90 percent, respectively).
  • In 2019, the percentage of adults age 25 and over who had not completed high school in remote rural areas (13 percent) was higher than the percentages in 8 of the 11 other sublocales, not including large cities, distant towns, and remote towns.
  • In 2019, the percentage of adults age 25 and over who had earned a bachelor’s or higher degree in remote rural areas (19 percent) was lower than the percentages in all other sublocales, which were as high as 38 percent in large cities and large suburban areas.

Explore more data on Public High School Graduation Rates in Rural Areas and Educational Attainment in Rural Areas.


Check out the Education Across America hub and the indicators linked throughout this blog post to learn more about how the landscape of education varies by locale/sublocale. Be sure to follow NCES on XFacebookLinkedIn, and YouTube and subscribe to the NCES NewsFlash to stay informed when new locale-focused resources are released.

 

[1] Related children include all children who live in a household and are related to the householder by birth, marriage, or adoption (except a child who is the spouse of the householder). The householder is the person (or one of the people) who owns or rents (maintains) the housing unit.

[2] Excludes mobile broadband, but includes all other non-dial-up internet services, such as DSL, cable modem, and fiber-optic cable.

[3] Although both round to 7 percent, the unrounded percentage of students who were Black in fringe towns was higher than the unrounded percentage of students who were Black in distant rural areas (6.9 vs. 6.8 percent).

[4] In fall 2019, the percentage of students in remote rural areas who were enrolled in public charter schools was 1.6 percent, compared with 1.9 percent for students in distant towns and 2.0 percent for students in distant rural areas.

[5] Ninety percent of graduates in distant towns, 93 percent in remote towns, and 95 percent in distant rural areas had taken at least one CTE course. These percentages were omitted from the discussion because they were not measurably different from the percentage for remote rural areas.

Celebrate LGBTQ+ Pride Month With NCES

Sexual minorities are people whose sexual orientation is something other than straight or heterosexual.

Gender minorities are people whose sex as recorded at birth is different from their gender.

June is LGBTQ+ Pride Month, and NCES is proud to share some of the work we have undertaken to collect data on the characteristics and well-being of sexual and gender minority (SGM) people. Inclusion of questions about sexual orientation and gender identity on federal surveys allows for a better understanding of SGM people relative to the general population. These questions generate data to inform the development of resources and interventions to better serve the SGM community. Giving respondents the opportunity to describe themselves and bring their “whole self” to a questionnaire also helps them to be more fully seen and heard by researchers and policymakers.

Sometimes, we get asked why questions like this appear on education surveys. They can be sensitive questions for some people, after all. We ask these questions so we can better understand educational equity and outcomes for SGM people, just as we do for other demographic groups, such as those defined by race, ethnicity, household income, and region of the country. Just as is the case for other demographic groups, it is possible that SGM people have unique experiences compared with students and educators from other demographic groups.

Over the past 10 years, NCES has researched how to best ask respondents about their sexual orientation and gender identity, how respondents react to these questions, and what the quality of the data is that NCES has collected in questionnaires and datasets that include sexual orientation and gender identity information.

Several NCES studies include background questions for adults about their sexual orientation and gender identity, including the High School Longitudinal Study of 2009 (HSLS:09) Second Follow-up in 2016, the Baccalaureate and Beyond Longitudinal Study (B&B) 08/18 and 16/21 collections, the National Postsecondary Student Aid Study (NPSAS) in 2020, the Beginning Postsecondary Students Longitudinal Study (BPS) 20/22 and 20/25 collections, and the 2023–24 National Teacher and Principal Survey. In addition, the School Crime Supplement (SCS) to the National Crime Victimization Survey (NCVS), conducted by the Bureau of Justice Statistics and sponsored by NCES, asks students several questions pertinent to SGM experiences. For example, the SCS asks students whether they were bullied due to their gender or sexual orientation and whether they experienced hate speech related to their gender or sexual orientation. As participants in the NCVS, students ages 16 and older who respond to the SCS also report their gender identity and sexual orientation. Collectively, these data allow NCES to describe the experiences of students who identify as sexual and gender minorities.

  • As of 2021, 2009 ninth-graders who were bisexual and questioning left postsecondary education without degrees or credentials at higher rates than other groups of students who were in ninth grade in 2009, and they earned bachelor’s or higher degrees at lower rates than other students.1
     
  • In 2020, some 9 percent of students who identified as genderqueer, gender nonconforming, or a different identity had difficulty finding safe and stable housing, which is the three times the rate of students who identified as male or female (3 percent each).2
     
  • In 2018, about 10 years after completing a 2007–08 bachelor’s degree, graduates who were gender minorities3 described their financial situations. Graduates who were gender minorities were less likely to own a home (31 percent) or hold a retirement account (74 percent) than graduates who were not gender minorities (63 percent and 87 percent, respectively).4
     
  • Among 2008 bachelor’s degree graduates with a full-time job in 2018, those who were straight people reported higher average salaries than those who were either lesbian/gay or bisexual.    
     
  • In the 2017–18 school year, 18 percent of public schools had a recognized student group that promoted the acceptance of students’ sexual orientation and gender identity, such as a Gay-Straight Alliance (GSA). This was an increase from the 2015–16 school year, in which 12 percent of schools reported having a GSA.5|
     
  • Among all students ages 12–18 in grades 6–12 who reported being bullied (19 percent), the percentage who reported being bullied due to their sexual orientation more than doubled from 2017 (4 percent) to 2022 (9 percent).6 That change was primarily driven by female students, for whom the percentage tripled from 2017 to 2022 (from 4 to 13 percent), while the percentage of bullied males who reported being bullied for their sexual orientation was not statistically significantly different across the period (3 percent in 2017 and 4 percent in 2022).

Figure 1. Among students ages 12–18 enrolled in grades 6–12 who reported being bullied, percentage who reported that they thought the bullying was related to their sexual orientation: 2017, 2019, and 2022

! Standard error for this estimate is 30 to 50 percent of the estimate’s value.

* Statistically significantly different (p < .05) from 2022. 


NCES is committed to collecting data about equity in education and describing the experiences of all students and educators, including SGM people.

To learn more about the research conducted at NCES and across the federal statistical system on the measurement of sexual orientation and gender identity, visit nces.ed.gov/FCSM/SOGI.asp.

Plus, be sure to follow NCES on XFacebookLinkedIn, and YouTube and subscribe to the NCES News Flash to stay informed when resources with SGM data are released.

 

By Elise Christopher, Maura Spiegelman, and Michael McGarrah, NCES


[1] SOURCE: Christopher, E. M. (2024). Disparities in postsecondary outcomes for LGBTQ+ individuals:
New evidence from the High School Longitudinal Study of 2009. Presented at the American Education Research Association Annual Meeting, Philadelphia, PA.

[2] SOURCE: U.S. Department of Education, National Center for Education Statistics, 2019–20 National Postsecondary Student Aid Study (NPSAS:20, preliminary data)

[3] On the NCES surveys mentioned above, gender identity categories include male; female; transgender, male-to-female; transgender, female-to-male; genderqueer or gender nonconforming; a different gender identity; and more than one gender identity.

[4] SOURCE: U.S. Department of Education, National Center for Education Statistics, 2008/18 Baccalaureate and Beyond Longitudinal Study (B&B:08/18).

[5] SOURCE: U.S. Department of Education, National Center for Education Statistics, 2015–16 and 2017–18 School Survey on Crime and Safety (SSOCS).

[6] SOURCE: U.S. Department of Education, National Center for Education Statistics, 2017, 2019, and 2022 School Crime Supplement (SCS) to the National Crime Victimization Survey (NCVS)

 

Celebrating the ECLS-K:2024: Participating Children Are the Key to Increasing Our Knowledge of Children’s Education and Development Today

As we highlighted in our March blog post, NCES is excited to be in the field for the base-year data collection of our newest national early childhood study, the Early Childhood Longitudinal Study, Kindergarten Class of 2023–24 (ECLS-K:2024). Although the study collects much needed data from a variety of adult respondents (i.e., parents/guardians, teachers, and school administrators), the heart of the ECLS-K:2024 is our data collection with the participating children.

With the permission of their parent/guardian, the children take part in the ECLS-K:2024 child session activities, answering engaging, age-appropriate reading and math questions during one-on-one sessions with trained ECLS-K:2024 team members (watch an example of children participating in the child activities). These ECLS-K:2024 child sessions are planned for every currently expected round of data collection, starting with the fall and spring of the current school year (2023–24) when the children are in kindergarten.

Although the child sessions look pretty similar every year, there are some changes to the activities as the children age. For example, in kindergarten and first grade, we include memory-related items in the sessions; we then swap out these items for child surveys in the later rounds, when children are in higher grades. In prior ECLS kindergarten cohort studies, child surveys included items on topics such as children’s sense of school belonging; worry or stress about school; media usage; and peer relationships. Explore the items we asked in the child surveys in the ECLS-K:2024’s sister studies, the ECLS-K and ECLS-K:2011. Many of these items will likely be asked again of the children participating in ECLS-K:2024. Also, in past studies, children had their height and weight measured to provide information about early physical development; this study component returns to the ECLS-K:2024’s spring data collection in some schools.

Child-provided data from the earlier ECLS program studies have been used extensively. A recent analysis conducted by the ECLS program team found that more than 1,000 studies and reports published between 2000 and 2021 have analyzed the ECLS academic skills and school performance data, with more than 80 percent of those utilizing the child assessment data. For example, NCES published a report on reading achievement over children’s early elementary school years using the ECLS-K reading assessment data. Use NCES’s Bibliography Search Tool to explore these reports (select “ECLS” from the Data Source drop-down menu).

If you’re instead interested in exploring trend data, research has been conducted on the differences between children who were in kindergarten during the 1998–99 and 2010–11 school years (use the Bibliography Search Tool to find reports on this topic). Additional research comparing kindergartners in the 1998–99 and 2010–11 school years with kindergartners in the 2023–24 school year is expected after this year’s ECLS-K:2024 data collection. Once the ECLS-K:2024 collection concludes, NCES will produce data files—made available to the public with deidentified data—that will allow for direct comparisons between these three groups of children. Our understanding of how the abilities of today’s kindergartners vary from those of kindergartners in the late 1990s and early 2010s relies on the participation of children in the ECLS-K:2024.  

Of course, it’s not just the children’s reading and mathematics data that will provide answers to key questions about education and child development. All the data the ECLS-K:2024 children are providing now in the study’s base year—as well as the data they will provide as they advance through their elementary years—will help inform our understanding of what today’s children know and can do.

On behalf of the thousands of researchers, policymakers, educators, and parents who rely on the important data provided by the ECLS-K:2024’s youngest contributors—thank you to our ECLS-K:2024 children!

Want to learn more?


Next up in this blog series celebrating the ECLS-K:2024, we’ll highlight the study’s parents and families. Keep an eye out this summer!

 

By Jill McCarroll and Korrie Johnson, NCES

Data on the High School Coursetaking of American Indian and Alaska Native Students

Understanding the racial/ethnic equity of educational experiences is a vital objective. The National Assessment of Educational Progress (NAEP) High School Transcript Study (HSTS) collects and analyzes transcripts from a nationally representative sample of America’s public and private high school graduates, including information about the coursetaking of students by race/ethnicity.

In 2019, NCES collected and coded high school transcript data from graduates who participated in the grade 12 NAEP assessments. The participants included American Indian and Alaska Native (AI/AN) students as well as students from other racial/ethnic groups. The main HSTS 2019 results do not include AI/AN findings because the sample sizes for AI/AN students in earlier collection periods were too small to report NAEP performance linked to coursetaking measures. Therefore, this blog post serves to highlight available AI/AN data. Find more information about NAEP's race/ethnicity categories and trends.
 

About HSTS 2019

The 2019 collection is the eighth wave of the study, which was last conducted in 2009 and first conducted in 1987. Data from 1990, 2000, 2009, and 2019—representing approximately decade-long spans—are discussed here. Data from HSTS cover prepandemic school years.
 

How many credits did AI/AN graduates earn?

For all racial/ethnic groups, the average number of Carnegie credits AI/AN graduates earned in 2019 was higher than in 2009 and earlier decades (figure 1). AI/AN graduates earned 27.4 credits on average in 2019, an increase from 23.0 credits in 1990. However, AI/AN graduates earned fewer overall credits in 2019 than did Asian/Pacific Islander, Black, and White graduates, a pattern consistent with prior decades.


Figure 1. Average total Carnegie credits earned by high school graduates, by student race/ethnicity: Selected years, 1990 through 2019 

[click to enlarge image]

Horizontal bar chart showing average total Carnegie credits earned by high school graduates by student race/ethnicity in selected years from 1990 through 2019.

* Significantly different (p < .05) from American Indian/Alaska Native group in the given year.                                                              
+ Significantly different (p < .05) from 2019 within racial/ethnic group.                                                   
NOTE: Race categories exclude Hispanic origin. Black includes African American, Hispanic includes Latino, and Pacific Islander includes Native Hawaiian.                                                               
SOURCE: U.S. Department of Education, Institute of Education Sciences, National Center for Education Statistics, National Assessment of Educational Progress (NAEP) High School Transcript Study, various years, 1990 to 2019.


In 2019, the smaller number of total credits earned by AI/AN graduates—compared with graduates in other racial/ethnic groups—was driven by the smaller number of academic credits earned. On average, AI/AN graduates earned about 1 to 3 academic credits less (19.3 credits) than graduates in other racial/ethnic groups (e.g., 22.2 for Asian/Pacific Islander graduates and 20.6 for Hispanic graduates) (figure 2). In contrast, AI/AN graduates earned more or a similar number of credits in career and technical education (CTE) (3.6 credits) and other courses (4.5 credits) compared with graduates in other racial/ethnic groups.


Figure 2. Average Carnegie credits earned by high school graduates in academic, career and technical education (CTE), and other courses, by student race/ethnicity: 2019

[click to enlarge image]

Horizontal bar chart showing average Carnegie credits earned by high school graduates in academic, career and technical education (CTE), and other courses by student race/ethnicity in 2019

* Significantly different (p < .05) from American Indian/Alaska Native group.                                                                            
NOTE: Race categories exclude Hispanic origin. Black includes African American, Hispanic includes Latino, and Pacific Islander includes Native Hawaiian.                                                                                                                                                            
SOURCE: U.S. Department of Education, Institute of Education Sciences, National Center for Education Statistics, National Assessment of Educational Progress (NAEP) High School Transcript Study, 2019.         
  



What was the grade point average (GPA) of AI/AN graduates?

As with credits earned, GPA has been generally trending upward since 1990. AI/AN graduates had an average GPA of 2.54 in 1990 and an average GPA of 3.02 in 2019 (figure 3). Unlike with credits earned, however, the average GPA for AI/AN graduates was between the GPA of graduates in other racial/ethnic groups in 2019: it was lower than the GPAs for Asian/Pacific Islander and White graduates and higher than the GPAs for Black and Hispanic graduates.


Figure 3. Average overall grade point average (GPA) earned by high school graduates, by student race/ethnicity: Selected years, 1990 through 2019

[click to enlarge image]

Horizontal bar chart showing average overall grade point average (GPA) earned by high school graduates by student race/ethnicity in selected years from 1990 through 2019.

* Significantly different (p < .05) from American Indian/Alaska Native group in the given year.                                            
+ Significantly different (p < .05) from 2019 within racial/ethnic group.                                                                                       
NOTE: Race categories exclude Hispanic origin. Black includes African American, Hispanic includes Latino, and Pacific Islander includes Native Hawaiian.                                                                                                                                                            
SOURCE: U.S. Department of Education, Institute of Education Sciences, National Center for Education Statistics, National Assessment of Educational Progress (NAEP) High School Transcript Study, various years, 1990 to 2019.



What curriculum level did AI/AN graduates reach?

HSTS uses curriculum levels to measure the rigor of high school graduates’ coursework as a potential indicator of college preparedness. There are three curriculum levels: standard, midlevel, and rigorous. Students who did not meet the requirements for a standard curriculum are considered to have a “below standard” curriculum.

Reflecting the smaller numbers of academic credits earned by AI/AN graduates, as described above, a lower percentage of AI/AN graduates reached the rigorous level (the highest level): only 5 percent of AI/AN graduates had completed a rigorous curriculum in 2019, compared with 10 percent of Hispanic, 13 percent of White, and 28 percent of Asian/Pacific Islander graduates (table 1). Similarly, a lower percentage of AI/AN graduates completed a midlevel curriculum than did White, Black, or Hispanic graduates. At the standard and below-standard levels, therefore, AI/AN graduates were overrepresented relative to most other groups.


Table 1. Percentage distribution of high school graduates across earned curriculum levels, by student race/ethnicity: 2019

Table showing the percentage distribution of high school graduates across earned curriculum levels (below standard, standard, midlevel, and rigorous) by student race/ethnicity in 2019.

* Significantly different (p < .05) from American Indian/Alaska Native group.
NOTE: Details may not sum to total due to rounding. A graduate who achieves the standard curriculum earned at least four Carnegie credits of English and three Carnegie credits each of social studies, mathematics, and science. A graduate who achieves a midlevel curriculum earned at least four Carnegie credits in English, three Carnegie credits in mathematics (including credits in algebra and geometry), three Carnegie credits in science (including credits in two among the subjects of biology, chemistry, and physics), three Carnegie credits in social studies, and one Carnegie credit in world languages. A graduate who achieves a rigorous curriculum earned at least four Carnegie credits in English, four Carnegie credits in mathematics (including credits in precalculus or calculus), three Carnegie credits in science (including credits in all three subjects of biology, chemistry, and physics), three Carnegie credits in social studies, and three Carnegie credits in world languages. Graduates with curriculum that do not meet the requirements for the standard level are considered as “Below standard.” Race categories exclude Hispanic origin. Black includes African American, Hispanic includes Latino, and Pacific Islander includes Native Hawaiian.
SOURCE: U.S. Department of Education, Institute of Education Sciences, National Center for Education Statistics, National Assessment of Educational Progress (NAEP) High School Transcript Study, 2019.


Explore the HSTS 2019 website to learn more about the study, including how courses are classified, grade point average is calculated, and race/ethnicity categories have changed over time. Be sure to follow NCES on XFacebookLinkedIn, and YouTube and subscribe to the NCES News Flash to stay informed about future HSTS data and resources.

 

By Ben Dalton, RTI International, and Robert Perkins, Westat