Supporting English Learners
Like strategies for reading in the content area and Universal Design for Learning, supporting English Learners (ELs) can benefit all students by using strategies to develop academic language.
Consider this statement:
All teachers are language teachers. Do you agree or disagree?
Does your thinking shift when you consider that there are four domains that make up language: reading, listening, speaking and writing?
Language is everywhere, in every content area and is an important part of student engagement in every class. The four language domains can be separated into two categories that sound very familiar to computer science educators - input (reading, listening) and output (speaking, writing). Our task as educators is to create input that is accessible and provide support and opportunities for meaningful output for all learners.
To get a sense for the English learner (EL) population in schools across the United States, let’s take a look at some demographic statistics:
English learners make up an average of 10% of K12 public school student populations, as of Fall of 2017 (NCES Data, 2020)
Spanish is the most widely spoken native language by ELs in the US, however many schools and districts serve students with a variety of home languages (NCES Data, 2020)
The average graduation rate for ELs was 60%, compared to 84% for non-ELs in 2015-16 (US Department of Education Data)
The Four Language Domains
Image source: Andrea Wilson Vazquez
We know there can be many barriers to English Learners’ participation in CS, and looking into the data for your state and school can be a helpful first step in starting the conversation. The following graphic comes from the 2020 State of CS report from Code.org and shows some states’ data comparing overall percent of ELs in their states vs. the % of ELs enrolled in CS courses. This is the first time this kind of data has been collected on a large scale. This data helps to demonstrate the urgent need for creating equitable and engaging computer science learning experiences for students from all linguistic and cultural backgrounds.
Consider the following 5 strategies for creating a more EL inclusive CS program:
Start with teacher professional development to ensure the availability of qualified staff to teach CS to ELs
Create early experiences and opportunities with CS that intentionally include ELs in order to create a more inclusive CS pipeline
Examine and redefine course placement policies to be inclusive of ELs in CS - work with counselors to ensure they are informed and on board
Involve ELs and their families in recruitment efforts and introductory activities
Support ELs' CS interests beyond the school day by partnering with CS professionals to create internships, career shadows, and other experiential opportunities
Language Development Overview:
Pause for a moment to consider all of the ways academic language is used in computer science. Also, consider that academic language support and development are necessary and helpful for English learners, as well as for any learner who may have less exposure to academic language. In fact, researcher Jeff Zwiers states that, “Academic language is often cited as one of the key factors affecting the “achievement gap” that exists between high- and low- performing students” (Zwiers, 2009).
An English learner first becomes proficient in BICS (Basic Interpersonal Communication Skills) - social language, simple sentence structure, high frequency vocabulary words and familiar content. ELs usually develop BICS in 1-3 years (J. Cummins, University of Toronto).
Proficiency in CALP (Cognitive Academic Language Proficiency) typically takes ELs 5+ years to master, and includes the language of lectures, formal, written text, specialized terminology, humor, non-verbal communication, idioms, social appropriateness and textbook language (J. Cummins, University of Toronto).
"Academic language is the set of words, grammar, and organizational strategies used to describe complex ideas, higher-order thinking processes, and abstract concepts." (Zwiers, 2009).
Academic Language & Vocabulary
Academic language can be understood through a helpful analogy: brick and mortar terms (Zwiers, 2009).
Brick terms are content-specific words that would normally be bolded in textbooks.
Mortar terms are general academic words and phrases that hold content-specific words together.
First, next, then, last
If, then, else
Supporting English Learners in CS
Watch the video to learn more about some strategies for supporting the academic language development of English learners, and more broadly, of all learners in your computer science courses. (Slides)
Here’s a quick review of the EL support strategies presented in the video:
Comprehensible Language Input
Opportunities for Meaningful Student Language Output
Metacognitive Analysis of Language Use and Development
Culturally Responsive Pedagogy
Part 1: Activity
Choose one lesson from Mobile CSP, CSAwesome, or another CS curriculum. Write down the content objective for the lesson, then consider what language functions students can use to show that they have mastered the content. Write at least 2 language objectives to go along with the content objective. (Jump to the content and language objective example in the video. )
Part 2: Reflection
Take a look at one lesson from Mobile CSP (or other CS course). Recall the academic language support strategies shared in this module related to input, output, structured collaboration, meta-analysis and culturally relevant pedagogy.
Consider the four language domains: speaking, listening, reading and writing. Write down examples of ways that students in your CS course are asked to engage with content through each of the language domains.
What academic language supports can you identify in the CS lesson you selected?
Where would you recommend adding additional academic language supports in the lesson you selected?
Name 3 specific academic language supports you would recommend adding to your CS lesson.
Academic Language Development:
Cummins: “BICS and CALP: Empirical and theoretical status of the distinction” https://www.researchgate.net/publication/226699482_BICS_and_CALP_Empirical_and_theoretical_status_of_the_distinction
Zwiers, J. 2009. Building Academic Language. California: Jossey-Bass.
AP CSP Exam Taker Data:
Code.org: "Dig deeper into AP computer science” https://code.org/promote/ap
College Board: "Number of females and underrepresented students taking AP computer science spikes again" https://www.collegeboard.org/releases/2018/number-of-females-and-underrepresented-students-taking-ap-computer-science-courses-spikes-again
Data on ELs in US Public Schools
NCES, "English language learners in public schools," 2020. https://nces.ed.gov/programs/coe/indicator_cgf.asp Accessed: 20 May, 2020.
US Department of Education, "Academic performance and outcomes for English learners," 2017. https://www2.ed.gov/datastory/el-outcomes/index.html. Accessed: 20 May, 2020.
ColorinColorado: "Academic Language Functions" https://www.colorincolorado.org/sites/default/files/Academic-Language-Function.pdf
Jacob, S., Nguyen, H., Tofel-Grehl, C., Richardson, D., Warschauer, M. (2018). Teaching computational thinking to English learners. NYS TESOL Journal.5(2), 12-24.
Vogel, S., Hoadley, C., Ascenzi-Moreno, L., Menken, K. The role of translanguaging in computational literacies. https://research.steinhardt.nyu.edu/scmsAdmin/media/users/ch97/publications/The_Role_of_Translanguaging_in_Computational_Literacies.pdf. Accessed: 22 May, 2020.
WIDA, "The English language learner can do descriptor booklet, grades 9-12," 2012. https://wida.wisc.edu/sites/default/files/resource/CanDo-Booklet-Gr-9-12.pdf. Accessed: 21 May, 2020.
WIDA, "The English language development standards," 2017. https://wida.wisc.edu/sites/default/files/resource/2012-ELD-Standards.pdf. Accessed: 21 May, 2020.
Zwiers, J., Dieckmann, J,, Rutherford-Quach, S., Daro, V., Skarin, R., Weiss, S., Malamut, J. (2017). Principles for the design of mathematics curricula: Promoting language and content development. https://ell.stanford.edu/sites/default/files/u6232/ULSCALE_ToA_Principles_MLRs__Final_v2.0_030217.pdf. Accessed: 21 May 2020.
This content was created by Andrea Wilson Vazquez