IES Blog

Institute of Education Sciences

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.

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.

I 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.

I’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.

 

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.

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.

My 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