IES Blog

Institute of Education Sciences

Identifying Virtual Schools Using the Common Core of Data (CCD)

With the sudden changes in education due to the coronavirus pandemic, virtual instruction is in the spotlight more than ever before. Prior to the pandemic, there were already increasing numbers of virtual public schools that offered instructional programs to those that may have difficulty accessing or attending traditional brick-and-mortar schools. Even before the pandemic, some schools and districts were using virtual instruction in new ways, such as switching to virtual instruction on snow days rather than cancelling school. Throughout the pandemic, schools and districts have been relying more heavily on virtual instruction than ever before.

Since school year (SY) 2013–14, the Common Core of Data (CCD) has included a school-level virtual status flag, which has changed over time. For SY 2020–21, the Department of Education instructed states to classify schools that are normally brick-and-mortar schools but are operating remotely during the pandemic as supplemental virtual (see table below).

 

SY 201314 Through SY 201516

Virtual status is a Yes/No flag, meaning that a school was either virtual or not virtual based on the following definition: “A public school that offers only instruction in which students and teachers are separated by time and/or location, and interaction occurs via computers and/or telecommunications technologies. A virtual school generally does not have a physical facility that allows students to attend classes on site.”

 

SY 201617 and Onward

NCES changed the virtual status flag to be more nuanced. Rather than just a Yes/No flag, the reported value indicates virtual status on a spectrum using the following values:

 

Permitted Value Abbreviation

Definition

FULLVIRTUAL

Exclusively virtual. All instruction offered by the school is virtual. This does not exclude students and teachers meeting in person for field trips, school-sponsored social events, or assessment purposes. All students receive all instruction virtually. Prior to SY 2019–20, this value was labeled as “Fully virtual.”

FACEVIRTUAL

Primarily virtual. The school’s major purpose is to provide virtual instruction to students, but some traditional classroom instruction is also provided. Most students receive all instruction virtually. Prior to SY 2019–20, this value was labeled as “Virtual with face to face options.”

SUPPVIRTUAL

Supplemental virtual. Instruction is directed by teachers in a traditional classroom setting; virtual instruction supplements face-to-face instruction by teachers. Students vary in the extent to which their instruction is virtual.

NOTVIRTUAL

No virtual instruction. The school does not offer any virtual instruction.  No students receive any virtual instruction. Prior to SY 2019–20, this value was labeled as “Not virtual.”

 

Generally, data users should treat the value “FULLVIRTUAL” (exclusively virtual) under the new approach as the equivalent of Virtual=Yes in the old approach. The virtual flag is a status assigned to a school as of October 1 each school year. 

The number of exclusively virtual schools has increased in the past several years. In SY 2013–14, there were a total of 478 exclusively virtual schools reported in CCD (approximately 0.5% of all operational schools). In SY 2019–20 there were 691 schools (approximately 0.7% of all operational schools) that were exclusively virtual. The student enrollment in exclusively virtual schools also increased from 199,815 students in SY 2013–14 to 293,717 in SY 2019–20, which is an increase from 0.4% of the total student enrollment in public schools to 0.6%.

Of the 691 virtual schools in SY 2019–20, 590 were reported as “regular” schools, meaning they offered a general academic curriculum rather than one focused on special needs or vocational education, 218 were charter schools, and 289 were high schools. Of the 8,673 schools that were reported as either primary virtual or supplemental virtual, 7,727 were regular schools, 624 were charter schools, and 4,098 were high schools.

To see tables summarizing the above data, visit our Data Tables web page and select the nonfiscal tables.

To learn more about the CCD, visit our web page. For more information about how to access CCD data, including tips for using the District and School Locators and the Elementary and Secondary Information System, read the blog post “Accessing the Common Core of Data (CCD).” You can also access the raw data files for additional information about public elementary and secondary schools. Enrollment and staff data for SY 2020–21 are currently being collected, processed, and verified and could be released by spring 2022.

 

By Patrick Keaton, NCES

Making the Most of a Quarantine Year: Meet the IES Virtual Interns!

April is National Internship Awareness Month, and we want to take this opportunity to highlight the Virtual Student Federal Service (VSFS) internship program that IES has been involved in this year and thank our wonderful interns for their contributions to the National Center for Education Research (NCER) and National Center for Special Education Research (NCSER).

The two IES Centers hired four interns to work on communication and two interns to work on data science. We asked each of them to tell us a little about themselves, their future plans, and what interested them or surprised them about the internship with IES. Here’s what they said.

 

Alice Bravo is pursuing a PhD in special education in the College of Education at the University of Washington.

Photo of Alice Bravo

My research interests keep evolving but are rooted in early intervention for young children with autism spectrum disorder (ASD) using applied behavior analysis and developmental science. Specifically, I am interested in the teaching of imitation and communication skills. In 5 years, I hope to be working as an applied researcher and practitioner, conducting research related to early intervention and ASD while providing training and coaching to caregivers and early intervention/early childhood special education professionals. During my internship with IES, I was really interested in and excited by the breadth of research supported by IES. Reading project abstracts related to virtual reality to support student learning was fascinating! 

Fun fact: I love road trips – I have driven up and down the West Coast and across the country twice! 

 

Bonnie Chan is pursuing a bachelor’s degree in statistics and machine learning at Carnegie Mellon.

Photo of Bonnie ChanI am interested in data science and modeling of data. I am interested in applying these approaches to research in the field of medicine or psychology because it has the most potential to help people and one of the most applicable uses of these approaches. As part of my virtual internship, I have learned how to use PANDAs Python package when cleaning data to prepare to create a visualization of grants funded by NCSER on a U.S. map. In addition, I learned a lot about how grants are funded by the department and the types of projects that are funded. In the future, I would like to pursue a master’s degree in machine learning or other statistical approaches for data science and modeling of data. I think working in the federal government would be a great experience and more rewarding in terms of outcomes than in the public sector or at an institution.

Fun Fact: I really like to dance. I have been dancing since I was 3, so that is 17 years. Right now, I mostly do contemporary dance, but I have done ballet, tap, jazz and other types of dance including competitive dancing in high school. 

 

Chandra Keerthi is pursuing a bachelor’s degree in data science at the Wilfrid Laurier University.

Photo of Chandra KeerthiI’m interested in applying statistical models of previous credit ratings to future ones in order to help model human behavior in the area of financial data analysis. I am also really interested in sports analytics, specifically basketball, and in understanding how analytics can help make or sometimes, unintentionally, break teams. In 5 years, I hope to use my skills to help create or innovate a product that will have a positive impact on the world.

Fun fact: I enjoy playing and watching basketball and am a huge fan of sci-fi movies and books (I’m currently reading the first book in the Dune series). In addition, I recently made a program that uses a photo taken from your phone and turns it into 'art' using another art piece (like van Gogh’s The Starry Night) as a reference.

 

Thomas Leonard is pursuing a bachelor’s degree in Economics and Business at Georgetown University.

Photo of Thomas Leonard

 

My research interest is in the area of finance. As a virtual intern, I had the opportunity to work on editing and examining abstracts across many different fields of education research, and this has sharpened my technical and analytical skills. In addition, it was interesting to see some of my experiences as a student actually being studied in schools across the country as part of the research that IES funds.

Fun fact: I’m an avid poker player. 

 

 

 

Yuri Lin is pursuing a bachelor’s degree in Microbiology, Immunology, and Molecular Genetics at the University of California, Los Angeles.

Photo of Yuri Lin

I am most interested in cancer genomics, immunology, and psychology. The most surprising detail that I had never thought about before this internship was how government entities like the Department of Education change and are influenced by different presidential administrations. In one of our monthly gatherings, we talked about how each administration has differing visions and values for education, and it struck me that while I saw myself as just a tired college student plinking away at blogs and abstracts in my bedroom, I was actually helping in small ways to fulfill a larger vision for education that sustains across administrations. That was a surprising and rewarding realization to have.

Fun fact: I love music, especially pop music and Russian classical music. There’s so much great music out there, but my favorite would have to be Shostakovich Symphony 5, Movement 4. Nothing feels quite like playing that piece in a huge orchestra with the cymbals crashing, and I hope everyone who hasn’t heard it before can go give it a listen.

 

Shirley Liu is pursuing a bachelor’s degree in English with a double minor in philosophy and data science at Lafayette College.

Photo of Shirley LiuMy research interests are in the areas of communication and data and information science. During this internship, I learned a lot about the human and community aspect of research. I have always viewed research and academia as very solitary fields. They are, but after talking to researchers about the friendships they’ve made in the field, I’ve learned that research is a lot more fruitful (and fun) when you’re doing it with someone whose company you enjoy. I really loved learning about Plain Language Principles! I have already started applying that to my own writing. For example, I am probably the only person in my friend group who knows what nominalization is and why it should be avoided.

Fun fact: My favorite hobby is writing! I have won an undergraduate-level prize for my poetry.

 


In addition to working on abstracts, entering data, creating data visualizations, and helping to update compendia of IES-funded research, our interns have also been busy writing blogs. Here are some recent blogs written by our interns: Autism Awareness & Acceptance Month; What Does This Mean for Me? A Conversation about College and ADHD; and Gender Stereotypes in STEM: Emergence and Prevention.

Highlights of 2015–16 and 2016–17 School-Level Finance Data

NCES annually publishes comprehensive data on the finances of public elementary and secondary schools through the Common Core of Data (CCD). For many years, these data have been released at the state level through the National Public Education Financial Survey (NPEFS) and at the school district level through the Local Education Agency (School District) Finance Survey (F-33).

Policymakers, researchers, and the public have long voiced concerns about the equitable distribution of school funding within and across districts. School-level finance data provide reliable and unbiased measures that can be utilized to compare how resources are distributed among schools within districts.

Education spending data are now available for 15 states[1] at the school level through the School-Level Finance Survey (SLFS), which NCES has been conducting annually since 2014.[2] In November 2018, the Office of Management and Budget (OMB) approved changes to the SLFS wherein variables have been added to make the SLFS directly analogous to the F-33 Survey and to the Every Student Succeeds Act (ESSA) provisions on reporting expenditures per pupil at the school and district levels.

Below are some key findings from the recently released NCES report Highlights of School-Level Finance Data: Selected Findings From the School-Level Finance Survey (SLFS) School Years 2015–16 (FY 16) and 2016–17 (FY 17).

 

Eight of the 15 states participating in the SLFS are able to report school-level expenditure data requested by the survey for a high percentage of their schools.

The initial years of the SLFS have consistently demonstrated that most states can report detailed school‑level spending data for the vast majority of their schools. In school year (SY) 2016–17 (FY 2017), most states participating in the SLFS (8 out of 15) reported school-level finance data for at least 95 percent of their schools (figure 1). With the exception of New Jersey,[3] all states were able to report at least partial SLFS finance data for more than 78 percent of their schools, ranging from 79 percent of schools in Colorado to 99 percent of schools in Oklahoma. In addition, the percentage of students covered by SLFS reporting was more than 99 percent in 9 of the 15 participating states. 


Figure 1. Percentage of students covered and percentage of schools with fiscal data reported in the School-Level Finance Survey (SLFS), by participating state: FY 2017


 

The SLFS can be used to evaluate school-level expenditure data based on various descriptive school characteristics.

The SLFS allows data users to not only view comparable school-level spending data but also evaluate differences in school-level spending based on a variety of school characteristics. In the report, SY 2016–17 (FY 2017) SLFS data were evaluated by charter status and urbanicity. Key findings from this evaluation include the following:

  • Median teacher salaries[4] in charter schools were lower than median teacher salaries in noncharter schools in all 7 states that met the standards for reporting teacher salaries for both charter and noncharter schools (figure 2).
  • School expenditures were often higher in cities and suburbs than in towns and rural areas. Median teacher salaries, for example, were highest for schools in either cities or suburbs in 9 of the 10 states that met the standards for reporting teacher salaries in each of the urbanicities (city, suburb, town, and rural) (figure 3).  

Figure 2. Median teacher salary for operational public elementary and secondary schools, by school charter status and reporting state: FY 2017


Figure 3. Median teacher salary for operational public elementary and secondary schools, by school urbanicity and reporting state: FY 2017


Median technology‑related expenditures per pupil were also highest for schools in either cities or suburbs in 9 of the 11 states that met the standards for reporting technology-related expenditures in each of the urbanicities, with schools in cities reporting the highest median technology-related expenditures per pupil in 6 of those states.

 

The SLFS can be used to evaluate and compare school-level expenditure data by various poverty indicators.

The report also evaluates and compares school-level spending by school poverty indicators, such as Title I eligibility and school neighborhood poverty level. Key findings from this evaluation include the following:

  • In SY 2016–17 (FY 2017), median teacher salaries were slightly lower for Title I eligible schools than for non-Title I eligible schools in 7 of the 8 states where standards were met for reporting both Title I eligible and non-Title I eligible schools. However, median personnel salaries per pupil were slightly lower for Title I eligible schools than for non-Title I eligible schools in only 2 of the 8 states where reporting standards were met.    
  • Median personnel salaries per pupil for SY 2016–17 were higher for schools in high‑poverty neighborhoods than for schools in low-poverty neighborhoods in 7 of the 12 states where standards were met for reporting school personnel salaries.

 

To learn more about these and other key findings from the SY 2015–16 and 2016–17 SLFS data collections, read the full report. The corresponding data files for these collections will be released later this year.


[1] The following 15 states participated in the SY 2015–16 and 2016–17 SLFS: Alabama, Arkansas, Colorado, Florida, Georgia, Kentucky, Louisiana, Maine, Michigan, New Jersey, North Carolina, Ohio, Oklahoma, Rhode Island, and Wyoming.

[2] Spending refers to “current expenditures,” which are expenditures for the day-to-day operation of schools and school districts for public elementary/secondary education. For the SY 2015–16 and 2016–17 data collections referenced in this blog, the SLFS did not collect complete current expenditures; the current expenditures collected for those years included expenditures most typically accounted for at the school level, such as instructional staff salaries, student support services salaries, instructional staff support services salaries, school administration salaries, and supplies and purchased services. As of SY 2017–18, the SLFS was expanded to collect complete current expenditures.

[3] In New Jersey, detailed school-level finance reporting is required for only its “Abbott” districts, which comprised only 31 of the state’s 699 school districts in SY 2016–17.

[4] “Median teacher salaries” are defined as the median of the schools’ average teacher salary. A school’s average teacher salary is calculated as the teacher salary expenditures reported for the school divided by the number of full-time-equivalent (FTE) teachers at the school. Note that this calculation differs from calculating the median of salaries across all teachers at the school, as the SLFS does not collect or report salary data at the teacher level.

 

 

By Stephen Cornman, NCES

Tips for Navigating the Digest of Education Statistics

Have you explored the recently released Digest of Education Statistics 2019? The Digest is a great resource for education data on a range of topics from a variety of sources. Because of the report’s size (the 2019 edition has more than 700 tables across 7 chapters), it can sometimes be a bit tricky to find the data you need. Read on to discover some tips for navigating the Digest website.

 

Finding the Most Recent Versions of Tables

There are several ways to find the most recent version of a table, depending on what page you are on. From the Digest landing page, click on “Most Current Digest Tables” (figure 1). From the list of tables page for a specific year, use the drop-down menu to select “Current” (figure 2).


Figure 1


Figure 2


  • Tip: If you navigate to a table from a search engine, you may not be on the most recent year. Check for a “Click here for the latest version of this table” option in the top righthand corner of the table’s page.

 

Finding Tables Using the Digest Indexing System

Have you ever navigated to a Digest table from a search engine or another NCES report and wanted to find other similar tables? What should you do if a Digest table covers a topic you want to learn about but does not include the specific data you need? You can use the Digest indexing system to find other similar tables.

The Digest indexing system is based on numbered chapters and topical subsections. Each table identifier contains the chapter number (1 through 7) followed by the subsection (e.g., 01, 02, 03) and the table number after the period (e.g., .10, .20, .30). For example, table 601.10 is the first table in chapter 6 under “Population, Enrollment, and Teachers,” which is subsection 01 (figure 3).


Figure 3


To find tables similar to the one you have found already, click on the link at the top left of the table’s page, which will take you to a full list of tables for the Digest edition you are viewing. For example, a Digest 2019 table will feature a “2019 Tables and Figures” link at the top left of the table (figure 4).


Figure 4


From here you can navigate to the relevant chapter and subsection using the indexing system. For example, if you were viewing table 601.10 and wanted to find other similar tables, you would click the + sign next to “Chapter 6. International Comparisons of Education” and then click the + next to “601 Population, Enrollment, and Teachers” (figure 5). You can then click through and explore all the relevant tables in that subsection.


Figure 5


  • Tip: Use the radio buttons at the top right to toggle between viewing tables and figures.
  • Tip: Use Ctrl + click to open tables in a new tab to avoid losing your place on the page.

If you do not see a table with the information you need, remember that the SOURCE note of a Digest table can be a great way to find related resources. Scroll to the bottom of a table to find the data sources used to prepare it.

 

Searching for Key Terms or Phrases Across All Tables

What if you want to search the Digest by topic instead of starting with a specific table? One easy way to do so is to navigate to the “List of 2019 Tables Page” and click “Show All” next to each chapter (figure 6).

  • Tip: Start at chapter 7 and work your way up to avoid having to scroll.

Figure 6


Once the tables are displayed for each chapter, you can easily do a global search of the entire page (Ctrl + F) and search for the term or phrase of your choice.

  • Tip: Try a few different search terms if you do not find what you are looking for right away. For example, the Digest uses the term “distance education” for remote learning.

 

Accessing Older Versions of Tables

In addition to the current edition of the report, the Digest website also contains archives of each report through 1990. You can access previous editions of the report in several ways (figure 7):

  • Select a year from the drop-down menu to access the HMTL versions of that year’s tables.
  • Click on “Access PDF versions of the Digest from 1990–2019” to access archived PDF files.
    • From here, you can click the + next to “Digest of Education Statistics” and select the report you want to view.

Figure 7


In addition to trying these tips for navigating the Digest website, you can explore the Reader’s Guide and Guide to Sources to learn more about the data sources used in the report. The Reader’s Guide also provides additional information about common measures and indexes, data analysis and interpretation, limitations of the data, and other aspects of the report. 

 

By Megan Barnett, AIR

Autism Awareness & Acceptance Month

April is Autism Awareness and Acceptance Month, a month dedicated to promoting true inclusion of individuals with autism spectrum disorder (ASD) and supporting them in reaching their full potential. In honor of this, we reached out to researchers aiming to improve outcomes for learners with ASD through Early Career Development and Mentoring grants from the National Center for Special Education Research (NCSER). We asked these principal investigators how they got involved in ASD research and about their current NCSER-funded work. Below is what they had to say.

Stephanie Shire, University of Oregon

Photo of Stephanie Shire

I first interacted with young children with ASD as a teenage volunteer in a hospital playroom. As I learned more about children with special needs through summer camps and as an in-home aide, I grew more intrigued by the range of strengths and needs of these children. I found joy in finding ways to connect with children who had few or no words, but I lacked the tools to support their growth. This set me on a path to learn about the range of intervention practices and intervention science under the mentorship of Dr. Connie Kasari at the University of California, Los Angeles. My overall goal is to develop and test intervention programs to support the deployment of high-quality practices across the United States and abroad.

In the spirit of this goal, the purpose of my Early Career project, LIFT (Leveraging autism Intervention for Families through Telehealth), is to develop a technology-enabled version of an evidence-based, caregiver-mediated social communication intervention (JASPER; Joint Attention, Symbolic Play, Engagement, and Regulation) to be delivered by community-based early educators serving families of young children with ASD in rural areas. We are currently in Year 1 of our 4-year project. This development year is focused on the creation of the online JASPER intervention and training materials for early intervention and early childhood special education providers. Despite the demands of the pandemic, participating providers have engaged in training using video and role play and the majority are now able to put their skills to use with young children with ASD. We are currently conducting user testing of the online materials and preparing for next year’s randomized controlled trial.

Veronica Fleury, Florida State University

Photo of Veronica Fleury

My first experience working with individuals with ASD was in a college course on behavior modification. The professor directed an ASD clinic that provided therapy using many of the strategies we discussed in class. I completed an internship in the clinic and was intrigued by the application of research techniques to promote prosocial behaviors for children with ASD. After college, I secured a full-time research assistantship at the University of Washington in a large ASD study focused on genetics and neurobiology. This was a pivotal experience because I realized this was not the kind of research that I wanted to pursue. The results of these efforts, while extremely valuable, did little to directly improve the lives of the participants. I realized that I wanted to be involved in applied research that allows for quicker uptake by practitioners and benefits for individuals with ASD. In order to be a good applied researcher, I needed practical experience working with children with ASD and their families. Although my entry into preschool special education teaching was initially a means to an end, it drew me in and further fueled my desire to serve children with disabilities. After this experience, I continued my graduate education and am now in an academic position that allows me to use science to address socially significant problems faced by individuals with ASD and their families.

The goal of Project START (Students and Teachers Actively Reading Together), which is part of my Early Career project, is to develop an adaptive shared reading intervention for preschool children with ASD using a sequential, multiple assignment, randomized trial (SMART) design. The results will help determine whether a full-scale efficacy study is worth pursuing for the intervention in its current form or whether additional refinement and testing is necessary.

Melanie Pellecchia, University of Pennsylvania

Photo of Melanie Pellecchia

I became interested in research focused on improving implementation of evidence-based treatments for young children with ASD in under-resourced communities after many years of working with young children with ASD as a behavior analyst overseeing publicly funded early intervention programs. While working within a large, urban public-service system, I observed the widespread disparities in access to high-quality intervention and challenges with implementing evidence-based interventions to scale for young children with ASD. I sought to pursue an academic research career focused on improving these implementation challenges.

As part of my Early Career project, I am iteratively developing a toolkit of implementation strategies designed to improve parent coaching for young children with ASD in Part C early intervention systems. I am currently in my third year of this project and am incorporating information learned from a variety of sources to develop the toolkit, including direct observations of early intervention sessions, qualitative interviews identifying barriers and facilitators to using parent coaching within early intervention, literature on best practices in parent coaching and parent-mediated interventions for young children with ASD, and feedback from expert and community advisory panels. The toolkit will include a series of infographics, videos, and a community of practice housed within an online platform. This year I plan to conduct a pilot study of the toolkit to assess its feasibility and promise for improving the use of parent coaching for young children with ASD in Part C service systems.

Marisa Fisher, Michigan State University

Photo of Marisa Fisher

Most people assume I got into the field because I grew up with an older brother with Williams Syndrome. But I didn't really think of myself as a sibling of a person with a disability and how that experience had shaped my life until I was in graduate school. The real reason I entered the field was because of three little boys with ASD with whom I worked as a behavior therapist when I was in college. What was originally a job became a passion for supporting people with ASD and other disabilities and finding better ways to teach skills and improve outcomes. I knew I wanted to go to graduate school, and it was my experience with these boys and my work at an ASD research lab that pushed me to pursue a doctorate in special education so that I could continue to work with people with disabilities.

Through my work with individuals with ASD, I began to realize the social struggles they and my brother experienced and became interested in studying experiences of social victimization and finding out why people with disabilities are more socially vulnerable than individuals without disabilities. The goal of my Early Career project is to do just that. A key part of this project involves assessing students’ self-reported bullying experiences. Although my original plan was to adapt and expand on existing measures, this didn’t result in a feasible assessment. Therefore, I turned my attention toward developing and testing an assessment that was appropriate for students with ASD and plan to use it to better understand the risk factors and consequences of bullying for these students. In general, my research is evolving from identifying and describing the risk factors to developing interventions to address those risk factors and reduce experiences of social victimization. My approach is to teach individuals with ASD to recognize and respond to situations and to evaluate ways to change attitudes toward individuals with ASD and improve social inclusion.

This blog was written by Alice Bravo, virtual intern for IES and doctoral candidate in special education at the University of Washington, and Katie Taylor, program officer for NCSER’s Early Career Development and Mentoring program.