11 Language Injustice in Computer Science Education

Chapter Overview

This chapter examines how inequities related to language manifest in computer science (CS) and CS education (CS Ed). It explores examples of how language injustice operates at ideological, institutional, interpersonal, and internalized levels in CS and CS Ed contexts. The chapter concludes by considering how CS educators can work toward language justice in their CS Ed settings.

Chapter Objectives

After reading this chapter, I can:

• Identify inequities related to language in CS and CS Ed.
• Explain how language injustice is manifested in CS Ed at the ideological, institutional, interpersonal, and internalized levels.
• Define language justice and give examples of language justice in CS Ed.

Key Terms:

bi/multilingual learners; the Four I’s of Oppression and Advantage; gatekeeping; intersectional identities; language ideologies; language injustice; language justice; raciolinguistic ideologies; standard English

John’s Story

Linguistic diversity is ever increasing in contemporary K-12 U.S. classrooms, including CS classrooms. Teachers may find themselves wondering how to reach all learners, regardless of how they communicate. This question is complex and multilayered and involves deeply understanding the students we are trying to support and the policies and beliefs about language that shape our educational spaces. To begin exploring these issues, let’s meet John, a sixth grade CS student.

As you read the introduction to John’s story below, consider these questions: What do you notice and wonder about John? How is he described here?

Language and Equity in CS Ed

Have you ever been judged for how you speak or write? Many people share the experience of feeling excluded when their way of communicating doesn’t align with how others are communicating. Sometimes this is because they misunderstood an “inside joke,” or felt out of place for not using the right “techie jargon,” or because they pronounced words differently than someone else. Others have had experiences of being mocked or stigmatized for their accent or for using languages or varieties deemed “inappropriate” to a particular situation.

While people may feel judged for their language in interpersonal interactions, inclusion and exclusion based on language are also structural features of our society. Language-based inequities occur systematically across many institutions, fields, and contexts, including education and CS Ed. Bi/multilingual learners, or students who use more than one language and who may be learning English, often experience significant marginalization in school.^[3] Bi/multilingual students may be separated from their peers in remedial-style English classes that have lowered expectations for academic learning and limited ways for bi/multilingual students to show what they do know. Standardized tests measure what students know and can do in English but fail to consider what students can express using other languages and modalities. Furthermore, bi/multilingual students may not have opportunities to learn CS at all. In 2022, for example, about 11.2% of students were designated English learners but only about 5.6% of them were enrolled in CS (100Kin10.org, 2022). In CS classrooms that do serve bi/multilingual learners, resources may not be provided in languages that students and their families can understand.

Bi/multilingual learners aren’t the only ones who experience marginalization due to their language and communication practices. CS programs may not have the resources or expertise to support learners with speech disabilities or other language-related disabilities. Educators may (even unwittingly) lower expectations for learners who have accents or speech patterns they perceive as “ethnic” or “uneducated” or for students who appear to not know specific technical or disciplinary vocabulary.

School systems and educational policies use many terms to label learners who are bi/multilingual and who are learning English at school. John, for example, was labeled as an English Language Learner (ELL) at his school. Each label used in education is attached to associations and assumptions about learners and educational expectations for them (García & Kleifigen, 2018). The terms have important consequences for students’ experiences in school.

Historically, the U.S. federal government has labeled bi/multilingual students with terms that take a deficit-based perspective on their abilities (e.g., limited English proficient, language-minority). This approach embeds those deficit views into U.S. schooling structures. Other labels center white, middle class, “standard” English language and culture as the desirable norm (e.g., ELL; culturally and linguistically diverse children; language minority students). In recent years, many school systems, states, and educators have worked to shift those perspectives by using terms that highlight both the assets of these students and the historical and ideological systems that marginalize them (e.g., emergent bi/multilingual; racialized bi/multilingual). A more comprehensive list of these labels and their associations appear in Table 1.

Throughout these chapters, you’ll notice that we use several of the terms in the table. We use “bi/multilingual learner” and “emergent bi/multilingual learner” to foreground students’ rich and fluid language practices. When we use “language-minoritized” or “racialized,” we seek to highlight how systems marginalize those who are perceived to have language practices that don’t conform to a dominant standard. In some cases, we may also refer to bi/multilingual learners who are receiving services for English learning at school as “bi/multilingual English Learners.”

Table 1
Language Labels in U.S. Educational Settings

Using different terms to refer to students is not just a semantic exercise or a case of “political correctness.” The terms that are used link the experiences of individual learners to the larger systems that are at the root of those experiences (Brooks, 2020; García, 2009b). CS educator Aaron shared what this looks like in his context:

While Aaron’s experience highlights the assumptions embedded in these terms, they also have real, material consequences for how students are taught. John’s ELL label meant that he was entitled to English learning support and services. In his school’s ENL program, a specialized teacher with training in Teaching English to Speakers of Other Languages (TESOL) was tasked with “pushing in” to content classes (e.g., math, science) to provide supports to students with ELL labels. The TESOL teacher also “pulled out” students, especially those just beginning to learn English, to a smaller classroom for differentiated activities a few times each week. John received both supports at different times throughout the week.

Local and state policies to support students with the ELL label vary and can have drawbacks that create gaps for students and their different needs. While being in a smaller pull-out ENL class might help students like John adapt to a new cultural and educational setting in a new country, being in those classes also means students miss out on opportunities, like computing, that are happening simultaneously in the general education classroom. In New York State, where John lives, one option to fulfill policy mandates for students with the ELL label is through bilingual education models. In this setting, students receive content instruction in both their home language and English and are not typically “pulled out” of content classes. There is a Spanish-English bilingual program at John’s school but not one that includes languages that John uses, which are less common in his neighborhood. Bilingual education is only required in New York City when more than fifteen students in two consecutive grades speak the same language. This approach creates gaps and differential learning opportunities for students who use a variety of languages but who may not live near others who share those languages.

John, like all students with the ELL label, is expected to make progress toward passing his state’s standardized English proficiency test each year. His school is evaluated based on his and his peers’ growth. This pressure could lead educators and administrators to focus solely on English learning at the expense of other content areas (Menken & Solorza, 2014). John’s school could run the risk of making English learning the most salient aspect of his identity, obscuring how he uses other languages in his daily life (Amharic, some Arabic, Tigrinya). Without care, school policies could end up segregating students like John from the general education population in an effort to push for standardized test performance, stigmatizing John throughout his schooling experiences. The realities described in this chapter give insight into the complex ways that language is entangled with issues of equity in CS Ed.

Language Injustice

The stigmas that can follow students in connection with language-related labels are symptomatic of a larger issue that shapes their experiences in schools, in CS Ed, and in CS fields: language injustice.

The details of John’s story illustrate some of the ways that language injustice plays out in school settings.

John’s story highlights that bi/multilingual learners are often navigating far more than their own communication goals when they decide which language to use in different situations. These students are grappling with schools, tools, and communities shaped by language injustice because society marks students’ language practices. Sometimes these practices are marked in ways that privilege students, but more often, language practices are marked in ways that marginalize students. Language injustices have been enacted against many learners whose language practices do not conform to the dominant group. This may include students who are Asian, Black, Latine, recent immigrants, children of immigrants, low income, language disabled, from rural areas, or some combination. For example, students who use creolized or vernacular-based varieties of English often don’t receive labels like those that appear in Table 1.^[4] However, because their language practices differ from the white, middle-class, “standard” English norm, they face language injustice and marginalization in school settings.

Language injustice manifests at the four levels of the Four I’s of Oppression framework we introduced in Chapter 5. It permeates society’s dominant ideologies, or systems of ideas about how society works. It also gets embedded in our institutions, shapes our interpersonal interactions, and gets internalized by individual people (Chinook Fund, n.d.). It is present in technology and computing fields, which use language to gatekeep and produce tools with embedded biases that fail to meet the needs of linguistically diverse societies. It is present in schooling and in CS Ed, where students who use — or who are perceived to use — language differently from the dominant “standard” English norm are marginalized. In the next sections, we examine language injustice at each of these four levels.

Ideological Language Injustice

One way that language injustice is perpetuated is through damaging language ideologies. Language ideologies are ideas, values, and assumptions about languages, language speakers, and language use that link language to broader social and political systems in different contexts (Irvine & Gal, 2000).

Teachers and students may notice how language ideologies influence the contexts around them. For example, John picked up on how English is centered in the United States, and how the language practices of white, upper- and middle-class “standard” English speakers are privileged over all other means of communication. The idea that using this kind of “standard” language indicates intelligence and capability is a language ideology, not an objective reality. Similar language ideologies exist in global contexts. For example, in Spanish-speaking communities, some Colombian or Argentinian varieties of Spanish are thought to be more prestigious than varieties from the Caribbean.

Deficit-based language ideologies are pervasive in the U.S. school system, and school is framed as a place to “fix” students and families who speak languages other than “standard” English. This is particularly pertinent to African American Vernacular English (AAVE). School cultures often misappropriate various AAVE terms as “slang,” which positions all usage of AAVE as “nonstandard” and “inappropriate” for academic settings or writing. This ideology not only limits students’ AAVE linguistic expression in the classroom but also broadly undervalues AAVE as a language. AAVE is framed as “a collection of slang terms” or as a “less-than-complete language,” instead of the full, robust language that linguists have identified it as (e.g., Smokoski, 2016). The effects of this language ideology have material impacts that marginalize and limit students’ authentic language use.

Language ideologies also intersect with other kinds of oppressive ideologies in our society, including ideologies about race. Language scholars Flores and Rosa (2015) introduced the concept of raciolinguistic ideologies to describe the politicized nature of how racialized bi/multilingual people are perceived by “white listening subjects.” These subjects are not so much individual people as they are the social norms that condition us to accept white, middle-class language practices as “standard.” Given students’ intersectional identities, students may be both racially marginalized and considered a language-minority, experiencing multiple and unique forms of marginalization.

Institutionalized Language Injustice

Oppressive language ideologies are also taken up by the policies and practices of institutions, becoming embedded in the tools those industries create. In the technology industry, the interfaces of many tech platforms are only available in English and a handful of other global languages. This limitation disadvantages many users and creators. For instance, Facebook suspended the accounts of many Native American users because Facebook systems did not interpret their names as “real names” (Holpuch, 2015). Voice recognition software is less effective at processing the language of people with “non-standard” accents (Paul, 2017). Software support for non-Roman scripts — or even for letters of the Roman alphabet that include accent marks and other symbols — is often absent, buggy, or prohibited (e.g., Fox, 2018). Artificial intelligence models have been shown to perpetuate extreme raciolinguistic stereotypes against speakers of AAVE. These dialect prejudices are more severe than any experimentally recorded human ones, as the AI models are more likely to suggest that speakers of AAVE “be assigned less prestigious jobs, be convicted of crimes, and be sentenced to death” (Hoffman et al., 2024, n.p.). These patterns demonstrate the impact of institutionalized language injustice.

CS educators Alexis and Jennifer described how their bi/multilingual learners have been impacted by embedded linguistic biases in technology. Alexis described how “my Bengali and Chinese-speaking students face more challenges in the classroom than my Spanish-speaking students do.^[5] Videos and subtitles are often not available in their native languages.” Jennifer added, “It’s definitely easier to translate with some languages than others. The keyboards for Russian and other Asian languages are different, and it makes translating harder.” These examples illustrate how English language-centric designs limit using technology to effectively support bi/multilingual students in CS classrooms.

Harmful language ideologies are also taken up by educational institutions, marginalizing some students and maintaining the linguistic supremacy of students who use language in “standard” ways. Historically across North America, Indigenous and Native American children were forcibly sent to boarding schools to “civilize” and assimilate them. These efforts included violently imposing English and punishing children for using languages other than English (Smithsonian, 2020; Suina, 1985). Violent means were also used to punish immigrant children for using home languages at school (García, 2009a). Some states continue to have laws on the books that make it illegal to educate students bilingually in public schools (Gómez, 2022), and other states have only repealed similar laws as recently as 2015 (Freedberg, 2016).

A common institutional practice is to educate students labeled as ELLs in “English-only” contexts. This approach perpetuates language ideologies that position bi/multilingual learners at a deficit. However, bi/multilingual learners are already fluent in varied and complex language practices. John, for example, spoke four different languages. Yet the ELL label emphasizes only students’ “standard” English language status, tying their intelligence to their scores on English proficiency exams and what they can demonstrate about their academic achievement using English. In some schools, schedules are designed to prioritize English learning over all other kinds of learning. As mentioned earlier, ENL teachers are often asked to pull bi/multilingual students out of class during CS time because CS is considered “enrichment,” but English learning is considered mandatory. Luckily for John and his classmates, their ENL teacher, Ms. Kors, took care to integrate CS into her class.

Even when students are able to access computing education, institutionalized marginalization can occur through assessments that depend on using the language of the dominant group. In one study, researchers found that middle schoolers designated as ELLs according to the state did worse than other students on text-heavy CS story problems but actually performed better on interviews related to open-ended, authentic final projects (Grover et al., 2016). These findings highlight how students who use language differently may be more successful than traditional metrics show. Bias may be embedded into these metrics in ways that do not enable students to demonstrate their actual abilities (Ascenzi-Moreno & Seltzer, 2021). The ways that language injustice manifests within institutions like the technology industry and computing education puts language minoritized users and learners at a disadvantage.

Interpersonal and Internalized Language Injustice

Language injustice also manifests interpersonally in interactions between people. In education, this might look like students teasing each other for their accents or for being in the ENL class. It could also appear when teachers tell Black students they are “articulate” when they don’t use AAVE. In CS industries, interpersonal injustice might include people might be excluded for not using the right tech jargon, not knowing the right nerdy cultural references, or not using certain programming languages. It might look like a tech firm hiring manager not hiring an engineer because their accent seems non-standard. For instance, April Christina Curley, a Black employee at Google, described how her manager told her that what they perceived as a prominent Baltimore accent was a “disability” that should be disclosed and that it “intimidated” others in the company (Duffy, 2020; Kirby, 2021). These technical contexts feed into broader methods of marginalization against those who don’t use language in the way traditionally dominant or powerful groups do. Language injustice also gets internalized by people, as speakers come to feel pride or shame in how they speak based on their experiences navigating systems and interactions.

Striving for Language Justice in CS Ed and Beyond

Bi/multilinguals and other language-minoritized learners come to school with experiences of language marginalization and with thoughts and opinions about it. Educators looking to promote equitable practices in their computing courses must grapple with language injustice. This might include discussing language injustice with students and becoming aware of how language injustice is part of the hidden curriculum in schooling. Educators can work to lower barriers for CS learners who are marginalized around language and to support all learners to notice and push back against oppressive language ideologies in tech tools and cultures. Disrupting inequities around language in CS Ed also involves striving for language justice.

Scholar April Baker-Bell (2020) writes specifically about language justice (or as she calls it, linguistic justice) in the context of supporting and sustaining the cultural and linguistic practices of Black students:

[Linguistic justice is] a call to action: a call to radically imagine and create a world free of anti-blackness. A call to create an education system where Black students, their language, their literacies, their culture, their creativity, their joy, their imagination, their brilliance, their freedom, their resistance MATTERS. (p. 2)

It is important to note that these linguistic practices are not limited to regional dialects of AAVE, as Black students, like all students, do not exist as a linguistic monolith. For Baker-Bell, linguistic justice is rooted in challenging white supremacy rather than uplifting any specific language practice. By working to dismantle anti-Black linguistic racism, she argues that educators can support Black students and, indeed, any speakers whose language practices deviate from white, middle-class, standard American English norms.

CS teachers can also promote language justice in their classrooms and spheres of influence. How might they do this? Teachers can learn to notice how language-minoritized students resist linguistic injustice daily, especially in the context of computing and technology activities. For example, at John’s middle school, students were asked to use a “personalized” automated learning software called iReady. iReady was only available in English, and students were expected to engage with it quietly and independently for test prep. Many students covertly conferred with each other, speaking in home languages and attempting to use translation software to support their use of the tool. One student, Andy, exercised critical consciousness, remarking that “programadores debieron pensarse como dos veces” (“programmers should think twice”) before releasing iReady as an English-only product. Andy’s classmate Mariposa added: “I think it’s racist because it doesn’t have two languages, it only have one. So it’s much difficult for kids that doesn’t know English.” Furthermore, the school didn’t have another technology class available. Andy remarked that if their classmates didn’t learn CS and coding, the students might think that computing was limited to iReady rather than encompassing more expansive and creative media (for more, see Vogel, 2020). These students’ actions resisted the language injustices they recognized at their school and in educational technology.

Instead of ignoring these moments or treating them as “off-task” tangents, teachers can build on students’ language practices and attitudes about language. Teachers who value language justice welcome the kinds of conversations that Andy, Mariposa, and John had around language, power, and technology. They support students to refine their critical consciousness around the potential biases of technology in relationship to language. Teachers can help students navigate choices related to language and technology in the CS classroom and beyond. They can provide opportunities to prototype or design new kinds of tools or digital artifacts to help students and their communities better learn, share, inquire, and express themselves.

Educators who care about language injustice can also become advocates outside of the classroom. There’s a long history of parents, educators, and communities fighting for linguistic justice, including the right to educate children using home languages.^[6] CS teachers can become involved at a systemic level by developing culturally and linguistically relevant CS curricula, ensuring that English learning services do not conflict with computing coursework, and considering the experiences of bi/multilingual learners and their families when making school policy decisions.

Revisiting John’s Story

John’s story in this chapter shows some of the ways that language injustice can operate in CS Ed. Even as a relative newcomer to U.S. schooling, John was already aware of how language ideologies embedded in institutional policies and structures influenced his interactions with those around him and shaped the language choices he made. At the same time, John’s varied and rich linguistic repertoire is a powerful resource for learning. In the next chapter, we’ll consider how John’s teacher, Ms. Kors, created opportunities for John to draw on his linguistic and cultural expertise as he worked on his Scratch family history project. Learning to recognize and center students’ language practices is a key part of moving toward more linguistically just ways of teaching and computing.

Reflection Questions:

1. What are some of your own language practices? Are there words, expressions, or language varieties that you use in some contexts or with some people but not others? How do you determine which language(s) or varieties to use and when?
2. Which languages or language varieties are welcome in your classroom or school? How do you communicate to students what ways of communication are (or are not) acceptable in different contexts?
3. What are some of the language ideologies that influence your CS context(s)? Where do you notice these ideologies appearing in institutional policies or interpersonal interactions? In the technology and computing tools you use?

Takeaways for Practice:

• Examine your school or district’s policies for language learners. Consider which terms are used as labels for students and what embedded assumptions and associations are connected to those labels. How do local policies and practices in your context reinforce or resist those assumptions?
• Consider the story about Andy, Mariposa, and John in the section “Striving for Language Justice in CS Ed and Beyond”. Choose a CS tool that is commonly used in your context. Analyze how linguistically accessible it is. What supports or modifications could you provide students — or have students develop — to make the tool more linguistically just?

Glossary

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