IES Blog

Institute of Education Sciences

Intersecting Identities: Advancing Research for Racialized English Learners

This year, Inside IES Research is publishing a series of blogs showcasing a diverse group of IES-funded education researchers and fellows that are making significant contributions to education research, policy, and practice. In recognition of Asian American and Pacific Islander Heritage Month, in this interview blog we asked Ben Le, an IES Predoctoral Fellow at New York University and a team member of the IES-funded R&D Center on the Success of English Learners (CSEL), to discuss his career journey and research interests.

How have your background and experiences shaped your scholarship and career in studying diversity, equity, and inclusion in education?

My research interests center around how race/ethnicity and language intersect to create unique privileges and discrimination. I hope my research can explore different ways we can support racially and linguistically marginalized students in schools, allowing them to bring their complete selves into the classroom and to help them thrive without having to give up their familial and communal languages.

Growing up in the United States as a Vietnamese-Mexican man has motivated me to look for new ways that we can conceptualize barriers for linguistically and racially marginalized students. While English learners (ELs) are currently the primary focus of my research, I’d like to recognize that I have never been classified as an EL.

I have been fortunate enough to be part of the IES-funded NYU Predoctoral Interdisciplinary Research Training (IES-PIRT) program, which has provided me the opportunity to  further explore and better understand racialized ELs’ access and opportunity in the classroom. My hope is that my IES-PIRT training will prepare me to work closely with local communities and organizations to enact change in our school systems. Ideally, we can build systems that truly support linguistically and racially marginalized students while offering them both access and opportunity that prepares them for life after school.

Can you tell us about your current IES-funded project?

As part of the CSEL R&D Center work, I am using a quantitative intersectional lens to highlight the importance of race/ethnicity for the diverse group of ELs in New York City public schools. I am particularly interested in how patterns of high school and college outcomes for current and former ELs vary based on race/ethnicity and gender. Focusing on 6-year graduation rates, I disaggregated my sample by race/ethnicity, gender, and ever-EL status (whether the student has ever been classified as an EL) to compare the probabilities across these subgroups and look for differential probabilities of being an ever-EL and a specific race/ethnicity. I focused on the two largest racial/ethnic groups of ELs in New York City, Asian Pacific Islander (PI) and Latine. For example, I compared the probability to graduate within 4 years between never-EL Asian/ (PI) young women to ever-EL Latino young men.

Interestingly, results, which were presented at the 2022 American Educational Research Association Annual Meeting, show that student probabilities for 6-year graduation are primarily organized by race/ethnicity, with Asian/PI students outperforming Latine students. Additionally, young women tend to outperform young men of their same racial/ethnic group, and in general, ever-EL status seems to matter even more for young men than young women. But these patterns do not explain away the racial/ethnic disparities seen in this New York City data. While ever-EL status matters, on aggregate, the ever-EL and never-EL differences primarily exist within racial/ethnic and gender subgroups. For example, never-EL Asian/PI young men outperform ever-EL Asian/PI young men, but ever-EL Asian/PI young men still outperform never-EL Latina young women.

Through my research, I hope to highlight the diversity and nuance within this ever-EL population, not to argue that ever-EL status does not matter. Instead, these findings have only motivated me to continue centering race/ethnicity and gender in future analyses for ELs.

What do you see as the greatest research needs to improve the relevance of education research for diverse communities of students and families?

From my perspective, we need to center the voices and concerns of these communities, families, and students in our data collection and analysis. I think it is essential to be involved with the families and meet them where they are to find effective solutions that benefit the communities we strive to serve. We need to make sure we are uplifting underserved families’ voices instead of talking over them. Relatedly, we need data and data collection to reflect the nuances and intricacies we are trying to discuss. Hopefully, future data collection can more accurately reflect the identities of the students we study. For example, I hope we can move away from collecting data as “male/female” and have a more expansive understanding of gender identities and not reify the gender binary.

What advice would you give to graduate students from underrepresented, minoritized groups that are pursuing a career in education research?

My first piece of advice would be to remember your own lived experiences and try to remind yourself that you do deserve to be in your graduate program. It’s easy to feel imposter syndrome—I think a lot of us do. Historically, academia and these programs were not made for us, and sadly, there is still a lot of work to be done, so that we don’t need to change to fit into these spaces. Still, these institutions and research fields benefit from our voices and perspectives. Remembering that these programs need us and that our experiences matter may be easier said than done, but I find it helpful to surround myself with fellow critical scholars and peers both within and outside of academia.

Secondly, finding community and support from peers and mentors has been absolutely crucial for my research and mental health. Doctoral programs aren’t easy; you are constantly being challenged intellectually and then you have to put your ideas and work out to be judged and critiqued. Being able to lean on friends and mentors for emotional support and to challenge and refine your research ideas is key to having a good and productive experience. I am super fortunate at NYU, through my sociology of education program and the IES-PIRT program, to have found such a caring community and supportive mentors, while also being pushed and challenged to pursue better and more critical work.


Produced by Helyn Kim (Helyn.Kim@ed.gov), program officer for the English Learners portfolio, NCER.

 

Releasing CCD Nonfiscal Data

The Common Core of Data (CCD) contains basic information on public elementary and secondary schools, local education agencies (LEAs), and state education agencies (SEAs) in the United States. The CCD collects fiscal and nonfiscal data about all public schools, public school districts, and state education agencies in the United States. Both IPEDS and CCD provide a sampling frame to many survey collections, including many conducted by NCES and the Department of Education. This blog post, one in a series of posts about CCD nonfiscal data, focuses on CCD’s two major releases and their corresponding components. For information on how to access and use CCD data, read the blog post Accessing the Common Core of Data (CCD).
 

Data Releases

CCD nonfiscal data are published in two releases every school year—as preliminary files and as provisional data files—within the CCD Data File tool. Understanding the differences between the two releases is important to understand how CCD nonfiscal data are released.

  • The preliminary files contain basic information about schools and districts, such as name, address, phone number, status, and NCES ID number. Many schools and districts utilize information from the directory file, such as the NCES ID, to apply for grants or other opportunities for their schools. Therefore, it is important that these files are released first, even if the data are still preliminary. 
     
  • The provisional data files are the full release of the CCD nonfiscal data. These data files provide school-, district-, and state-level data on topics like enrollment, staffing, and free or reduced-price lunch. These files are much more detailed and include data that are broken down by characteristics such as grade, race/ethnicity, and gender—as well as by combinations of these characteristics. These files are not updated unless there is a significant change to the data.

Each file release includes a version that indicates the type of release. The first preliminary files have “0a” in the file names, and revised preliminary files include “0b,” “0c,” and so on. The first provisional files have “1a,” in the file names, and revised provisional files include “1b,” “1c,” and so on. Note, however, that releasing revised files is rare.
 

Components of a Release

It is important to utilize the various components that accompany each release to find additional information that is specific to the file and can help you better understand the data. In addition, there are other resources available that provide more ways to access and understand the data.
 

Documentation Components

Every data file will have documentation files that provide information about the data. These include the following:

  • release notes—basic information about the data release, including details about any changes to the files, such as a change in a variable’s description or a variable that was added to the file; summary tables that include national totals and tables with selected frequencies are also included.
     
  • state data notes—information on data anomalies that are discovered during NCES’s collaboration with the states; broken down by state and by file type, these notes describe things like changes to how data were collected by the state.
     
  • companion files—included in each data file component, these files include a list of all the variables in the data file—including a brief description—and frequency tables; you should start with the companion files to better understand what variables are in each data file.


Resources and Tools

Along with the release of the CCD nonfiscal data files, additional resources are also updated to improve access to the data.

  • Summary Tables: Released with the provisional data files, Summary Tables provide a national-level look at the data. These tables show the operational status of schools and districts by type as well as the number of schools, students, and teachers by state.
     
  • Locators and ElSi: There are two primary tools that can be used to access CCD data: the Locators (School Locator and District Locator) and the Elementary/Secondary Information System (ElSi). These tools are updated as the data files are released. The Locators are updated with each release, while ElSi is updated with the release of the provisional data files. Learn more about these tools.
     
  • Online Documentation: The online documentation provides some general information about CCD. This information is not year specific, but it provides a detailed explanation about how the data are collected, processed, and reviewed.
  • Reference Library: The reference library includes detailed documentation on various components of the CCD files that applies to multiple years, levels, and components of the data collection. The library includes crosswalks, documentation describing changes to the collection, and guidance for utilizing the data files, such as how to aggregate free or reduced-price lunch data.

Be sure to follow NCES on TwitterFacebookLinkedIn, and YouTube and subscribe to the NCES News Flash to stay up-to-date on future CCD releases and resources.

 

By Patrick Keaton, NCES