While a growing number of initiatives around the country are focused on expanding access to computer science (CS) education, the inclusion of students with disabilities has received little attention in such initiatives.[i] In fact, as a field, the K-12 CS education community has been reluctant to track the participation of students with disabilities in CS education initiatives.[ii] The few studies that examine students with disabilities do so in aggregate, which masks differences by grade level and disability classification.[iii] These factors have limited our understanding of the extent to which students with disabilities are included in K-12 CS education. Information about CS participation by grade level and disability category is particularly important to inform meaningful decisions about pedagogical approaches that can meet the needs of all students in K-12 CS education.
This Spotlight post uses data on students’ Individualized Education Plans (IEPs) and CS course enrollment records from the 2018-2019 school year to explore students’ participation in CS education across grade bands. It is important to note that the analyses below include only students who attend public schools in one of NYC’s 32 community school districts. They do not include students who attended alternative high schools or charter schools, or students who are served in District 75, a special district in the City that serves students with low-incidence disabilities[iv] or highly specialized needs.
In 2016, New York City, like many districts and states around the country, responded to the call for K-12 CS education with a Computer Science for All initiative. The initiative’s central goal is to ensure that all public school students—especially female, Black, and Latinx students (who historically have been underrepresented)—learn computer science. Specifically, the initiative aims to provide at least one meaningful and high-quality CS experience in each grade band: Kindergarten to 2nd grade, 3rd through 5th grade, 6th through 8th grade, and 9th through 12th grade.
Through the initiative, schools are encouraged to implement CS instruction in a way that aligns best to their educational vision. At the elementary level, 16- to 20-hour CS units are incorporated into core subject areas (e.g., science, mathematics, social studies, English language arts) or classes like art, music, or technology. At the middle and high school level, CS can be a semester course, a multi-year sequence, or incorporated into other content-area courses (e.g., science, math, art). Thus, students’ CS experiences may vary widely, ranging from a 16-hour integrated unit that engages K-2nd graders in remixing and creating visual media, to a 54-hour introductory project-based course that uses Scratch programming for middle school students, to a 108-hour advanced placement course for 10th to 12th graders.[v] The analyses below include participation in any of these types of CS experiences.
As part of the Research Alliance’s evaluation of the CS4All initiative, we have been examining progress toward equitable CS participation in NYC schools. Promisingly, since the initiative began, there have been year-by-year increases in the number of schools offering and students taking CS courses, including a steady rise in Black, Latinx, and female student participation. Further, we have seen a four-fold increase in the participation of students with disabilities (from 4% to 16%) between 2016 and 2020 (see, for example, these reports). We hope the analyses below are useful to educators and district leaders, as they continue working to expand access to CS education in NYC schools.
Source: Research Alliance calculations based on data obtained from the NYC Department of Education.
These findings reflect only the beginning of the work needed to understand the participation of students with disabilities in K-12 CS education. The literature points to many possible reasons for differences in opportunities among students with and without disabilities, including institutional and teacher bias in perceptions of who should take CS, as well as lack of teacher supports and resources to effectively serve students with diverse needs.[vi] In addition, students with disabilities may have more difficulty fitting CS courses into their schedule alongside needed services such as speech, occupational, or physical therapy. The current findings raise a number of key questions that we will be exploring in our future work, including:
- What factors influence CS participation for students with disabilities? For example, to what extent is participation influenced by attitudes and perceptions about who CS is for?
- What is the relationship between CS participation, disability status and other demographic factors such as race/ethnicity, socioeconomic status, and gender? Are the disparities we see in CS participation among general education students also present among students with disabilities?
- Is the participation of students with disabilities in K-12 CS education related to school-level factors such as neighborhood/school SES, the percentage of students with disabilities in the school, the percentage of students with disabilities served in inclusive or self-contained settings, or the number of computer science classes offered in the school?
- What types of CS classes are students with different disabilities taking? For example, at the high school level, are students with disabilities represented in Advanced Placement CS classes?
This post was authored by Cheri Fancsali and Maya Israel (Educational Technology, The Creative Technology Research Lab, University of Florida), with analytic support by Zitsi Mirakhur and Ethan Crasto. The analyses were conducted as part of our ongoing study of the NYC CS4All initiative, in which the Research Alliance is answering important questions about the implementation and impact of the initiative for NYC students, schools, and teachers. The study is supported by the Fund for Public Schools.
[i] Bouck, E. C., Sands, P., Long, H., & Yadav, A. (2021). “Preparing Special Education Preservice Teachers to Teach Computational Thinking and Computer Science in Mathematics.” Teacher Education and Special Education, 0888406421992376.
Ladner, R. E., & Israel, M. (2016). “ ‘For all’ in ‘computer science for all’.” Communications of the ACM, 59(9), 26-28.
[ii] Blaser, B., & Ladner, R. E. (2020, March). “Why is Data on Disability So Hard to Collect and Understand?” In 2020 Research on Equity and Sustained Participation in Engineering, Computing, and Technology (RESPECT) (Vol. 1, pp. 1-8). IEEE.
[iii] Code.org, CSTA, & ECEP Alliance. (2020). 2020 State of Computer Science Education: Illuminating Disparities. Retrieved from https://advocacy.code.org/stateofcs.
[iv] Low-incidence disabilities are visual or hearing impairments, significant cognitive impairments, or “any impairment for which a small number of personnel with highly specialized skills and knowledge are needed in order for children with that impairment to receive early intervention services or a free appropriate public education” (IDEA section 1462 (c) https://sites.ed.gov/idea/statute-chapter-33/subchapter-IV/part-B/1462/c)
[v] See https://blueprint.cs4all.nyc/curriculum/ for a description of CS4All’s K-12 curriculum.
[vi] Kirby, M. (April, 2017). Implicit assumptions in special education policy: Promoting full inclusion for students with learning disabilities. In Child & Youth Care Forum (Vol. 46, No. 2, pp. 175-191). Springer US.