There exists plenty of literature about how to be inclusive, both in the workplace and in the classroom.

I'm wondering how this might be done intentionally — specifically in the context of a computer science class — from a curricular standpoint. I'm thinking of generic intro-to-programming types of courses, which usually include algorithms and data structures.

I have attended several workshops and have read a few articles, but haven't really delved into the literature about inclusivity in the classroom. From these workshops, I am quite comfortable being inclusive in the classroom (lectures, interactions, etc.) -- but I'm looking for ways to have that reach to homework, projects, and so on -- and specifically for computer science as far as possible.

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    Have you read the existing literature on inclusivity in a classroom? Which ideas can be applied in the computer science lab as well? Commented Oct 28, 2019 at 2:23
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    I don't get the downvotes on this. OP clearly asks for a "curriculum" point of view, which is highly relevant and absolutely non-trivial.
    – xLeitix
    Commented Oct 28, 2019 at 13:30
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    @xLeitix: The question is broad and vague even if it sounds precise. What kind of curriculum? Major or minor? Intended for what careers? (These are important questions; e.g. the answer by deags silently presumes that the students will go into big data, or at least that the part of the student population that does not is negligible.) Commented Oct 28, 2019 at 18:22
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    @AzorAhai: I saw some of the "things" you refer to recently, but I am optimistic that we can build a supportive and inclusive community regardless of imperfect efforts on the part of the company that owns this site. The SE community does not consist only of the SE company. We make it what we want. I choose to be inclusive regardless of those around me. If SE follows suit, they get me as a user. If they choose to not be inclusive (not just small mistakes due to individuals, but repeated, serious mistakes on the corporate level), they lose me. I'm small, but there are others like me.
    – jvriesem
    Commented Oct 28, 2019 at 18:31
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    Including who? Foreigners, disabled, minorities? Commented Oct 29, 2019 at 17:33

6 Answers 6


This is an interesting question. The program I am doing most of my teaching in has recently been faced with a similar question (due to comments of an external evaluation), and I can't say we have come up with a satisfying solution yet.

Here are some pointers (in no particular order) that have come up in our discussion:

  • For exam tasks / homework descriptions: evaluate critically if your tasks (or rather, their descriptions) carry bias. Naturally, you want to avoid the cool developer being called Bob and the silly user Alice or Ahmed, but it goes deeper than that. For example, in an Intro to Programming exam I was giving many years back the task was to model and implement a simulation of a game of football/soccer (the assumption here being that the majority population - white, male college kids in Europe - would be intimately aware of the rules and have an easy time conceptualizing the task). As it turns out, those that did not fall into the majority population (some women, foreign students) did worse on this task, presumably because they needed to spend a lot more brain cycles even understanding the game they were supposed to simulate.
  • For deadlines: consider that some students may not be able to, if need be, work through the night or the weekend. It's easy to think that a deadline Sunday evening is not a big deal if you think of your students as a homogenious mass of 20-year-olds with no other obligations than to study, but if one of your students is a father / mother of two, or working on the weekend to sustain themselves, the story becomes different.
  • For extra-curricular activities: the same is also true for extra-curriculars. If at all possible schedule attractive extra-curriculars (e.g., Hackathons, job fairs, whatever your university does) in a way that they can also be attended by people outside the majority population.
  • For social events: the same applies to social events. Make sure that social events, to the extent possible, not only cater towards the majority population. For instance, when I studied, a lot of the student social events boiled down to "find an excuse to drink", and basically all conversation was in the local language. Both of this together unsurprisingly meant that foreign students, especially Islamic students, never showed up.
  • Avoiding "hidden knowledge": in many universities and courses, there are official rules and "unspoken rules". For example, a teacher may say that students are expected to learn the entire book, but well-connected members of the majority population know from previous years that in reality most of the questions are about Chapters 3-8. Given that (some) minorities are less well-connected than the majority population, these constructions can disadvantage these students severely.
  • Diversity of TAs: ensure that not all TAs are always from the majority population (i.e., ensure that there is some amount of representation of non-majority students). This may need active steering, because if you just take the first 5 qualified students, you may easily end up with only students from the majority population (because they are more, because they are better-connected, and because a lot of TA contracts are written in a way to appeal to 20-year-olds).

To summarize: keep in mind that some of your students may have a different cultural background, different life constraints, different priorities, or different access to "common knowledge". Try to level the playing field as well as you can.

  • Comments are not for extended discussion; this conversation has been moved to chat. Commented Oct 30, 2019 at 17:44
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    About the diversity: As someone who was a tiny minority of one (woman in a class of ~50) at the end of her degree, I would have damn well appreciated if not every person I encountered in a professional capacity was a 20 years younger version of the white male middle aged professors. Back then I thought it wouldn't matter, but 20 years on I know it did. It's very dispiriting to see that the same type of person gets hired over and over again, because they are the best fit by construction. I don't want any special handouts, I want a level playing field and it's not there. Commented Oct 31, 2019 at 10:50
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    (There was an improvement suggestion coming up...) @xLeitix: Maybe you could find a reference on how implicit bias screws up hiring decisions. From my experience if you have a group that is too homogeneous, you miss out on a lot of possible approaches to problems that might actually be better than the ones you have learned in your limited eco system. Commented Oct 31, 2019 at 10:54
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    @Allure There is also a cultural and language aspect here. When a group of people go out to "grab a beer", it is (or at least should be) perfectly acceptable to join without drinking any beer. I remember reading an interview with a Muslim student who said he wish he realised this earlier. (My own undergraduate major uni in The Netherlands incidentally failed at almost all of the points mentioned in this post, when I moved to Sweden it was much more inclusive)
    – gerrit
    Commented Oct 31, 2019 at 15:12
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    The thing about deadlines is, it's not the deadline, it's the start date. Give students enough time and the deadline date is irrelevant.
    – Bob Brown
    Commented Nov 1, 2019 at 0:28

I expect that most modern schools will have many special-interest groups for every minority and sexuality, gender and religion. This attitude has no place in the classroom. What classes really seem to need in terms of inclusivity are the following:

  • Inclusivity of ideas. Don't inject snarky political comments into your lectures under the assumption that everyone has the same opinions. This alienates students who have different ideas and opinions, and gets old quickly. I was in school during the 2016 election. I was about ready to die by graduation.

  • Inclusivity of learning styles. On tests and exams, include different types of questions, and award points for answers that don't quite hit the mark but show evidence of critical or logical thinking, as these are the underpinnings of computer science. Even if the student doesn't remember the exact algorithm in question, showing that he can think about it in a "CS-y" way proves that he is the right kind of student for the program. This also can be extended to lectures and additional readings. Provide examples that are visual. Provide examples that are auditory. Create analogies and parallels. And make sure that the board is visible all throughout the classroom and that you can actually be heard all throughout the classroom. If a lecture hall is so big that the students in the back have a significant disadvantage, that's because there are supposedly that many students in the lecture, so don't punish kids who have to sit in the back.

  • Inclusivity of schedules. While the point about setting deadlines to avoid weekends or nights is not really practical due to the aforementioned snowball effect, you can try to make sure that someone is available in some capacity for a lot of time. Try to respond promptly to emails, or have a TA who responds quickly to them. Keep up with Piazza. Nothing is more disappointing than a class message board that hasn't been viewed by an instructor in over a month. To take this a step further, please keep up with your grading. Some students are not all right with having to wait until the day before the final to know how they're doing in the course. Many students are planners, and want to have as much information as possible as early as possible to make informed decisions about how to allocate their study time.

  • Comments are not for extended discussion; this conversation has been moved to chat.
    – cag51
    Commented Oct 31, 2019 at 0:35

tl;dr It's not so much about curriculum, but in delivery and materials.

I have specialised in issues of inclusivity in Computer Science within my university and have several publications in this area. Many of my papers look at inclusivity for students with alternate needs but I have also been looking at issues of gender issues that affect inclusivity.

The question asks what changes in a curriculum should one make? It depends somewhat on how you define curriculum and the scope of what a curriculum contains, which in my experience varies from place to place (or even discipline to discipline). In theory a curriculum is a document that defines higher level outcomes, competencies and broad brush subject areas. For example, it would say that students should be taught computer programming but not specify a language or software platform; it might say computer architecture without naming specific hardware devices and so on.

Thus we should have a universal curriculum that makes it clear what would be learned and demonstrate the value of the qualification without saying in detail the mechanisms by which those goals would be achieved (or am I being too theoretical in my pedagogics?).

What does need addressing to permit inclusivity in a computer science curriculum are aspects of delivery: the teaching material, the environment, the language, the people, the attitude. Many of these are human focused aspects of education.

There are probably too many detailed elements for me to include them all, but some important ones are:

Hearts and Minds: If your colleagues are not totally engaged in the diversity agenda, or see value in it then much effort in this direction would be attenuated. Everyone has to agree; the problem is convincing them. One has to point out that it helps recruitment, reputation, retention, income generation, student morale, improved results and so on. For the recidivists I just pointed out that to do otherwise would probably be illegal and perhaps result in embarrassing legal cases at some indeterminate time in the future, and would they like to explain their position in court? (That worked).

Language (spoken and written): Everyone has to be able to use appropriate language automatically and without undue effort. All class material should use appropriate language, and all verbalisations in class should be appropriate. Some examples from the past: do not assume programmers or manager would be male by slipping into "when he ...". Do not assume everyone can see the screen by saying "as you can see in the diagram..." (vision impairment) or assume that everyone is able to hear clearly, and so on. Use multiple forms of delivery in any class.

Images and Illustrations: Be careful in the selections of images used in teaching material. What do the images imply: like the languages do they imply particular attributes for the people pictured; particularly ones that not all students could relate to (e.g. all men etc).

Use Technology: As we are talking about computer science there is no excuse for not using technology. There should be online support for the classes (like a VLE) so students can download notes, copies of the presentations and even video recordings of the class. This can also allows assessment submission and feedback. This permits student with differing needs to use their own support technology to access the material without extra spport.

Unconscious Bias: Teachers needs to accept that they have implicit unconscious Bias use that awareness in their work. Colleagues might need to attend the available training.

Culturally Specific Examples: To help students understand difficult concepts examples and exercises are important. Picking the right examples will help learning. However there are cultural traps in these example which one should avoid because they might implicitly exclude or disadvantage a particular group. Assuming that students know how tax works or using abbreviations for tax forms or assuming they know something outside the subject like complex numbers or eigen vectors can be difficult.

Physical Access: An obvious one when considering diversity, but labs need adjustable height desks, doors wide enough for adult wheelchairs there need to be appropriate toilet facilities, appropriate break and rest areas, quiet and social spaces. All these address the needs of different groups. For example, our campus lacks a fully equipped disabled access toilet (i.e. one with a power hoist).

Regulations and Procedures: Are the institutional regulations for things such as attendance, submission of mitigation, repeat years and such like appropriately accommodating for the various needs of student groups or are they discriminatingly punitive in an attempt to be equal.

The wider institution: It is as important that management, IT provision and marketing for your institution are similarly minded. Without them many of the efforts in one subject area would just be made impotent by the lack of support from elsewhere.

Although not answering the question directly you may be interested in publications in this area. As has already been mentioned in comments, there is plenty of literature out there that you can refer to. Mine is but a splash is the ocean:

I hope that of that would be useful at pointing you at issues in this area.

  • Since you're an expert, I would very much love to hear more about the state of this field as you see it. No pressure — I respect that your valuable time is in short supply. :-)
    – jvriesem
    Commented Nov 1, 2019 at 1:21
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    Updated: For the curious, there are meta comments embedded therein. Commented Nov 1, 2019 at 15:26

To add to the other answers, I'd like to point out that at least 10% of the general population has some sort of significant learning difference such as ADHD or a specific learning disability. If "learning differences" include things like ASD, social anxiety, sensory issues, and psychiatric conditions, the percentage is much higher. So avoid creating one-size-fits-all courses. Provide multiple ways for students to access material and participate in the course and multiple ways for them to develop and demonstrate mastery.

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    I strongly agree. I cannot both write and think. If I am taking notes, I am not understanding and thinking about the material, I am just taking notes. Some lectures seem to be structured to force note taking, which helps many students but is really bad for me. Commented Oct 30, 2019 at 7:13

Important Notice

Be careful about what you have read, a lot of grievance studies "papers" are based in faith not science. And do more harm than good. Here is a very good "study" done by two Phds and one journalists about the validity of gender studies, critical race studies etc. It is scary.


To answer your question:

Try to use simple language if possible if you have non native speakers. Of course do it only when complicated terms are not needed to describe what you want to teach.

Make sure people don't write their name but their id number on their homework to avoid bias.

If you employ TAs try to hire them blindly, have someone send you their job applications without any personal information like name, age, etc. .

If you are doing some activities plan for different preferences. Provide alcoholic and non alcoholic drinks. Bring a bbq grill for vegans, vegetarians and omnivores and provide food for each of them.

Make sure people in a wheelchair can enter the location.

Make sure their are different areas for people with different needs. Some people are epileptic or have hyperacusis (can't be in loud locations).

Make sure everyone is invitited via an official mailing list.

Respect the freedom of all people. So don't overdo it. If you ban alcohol to please muslims, you are infringing on the freedom of non muslims. If you ban meat to please the vegans, you are infringing on the freedom of omnivores. You can have two tables, one with beer and one with lemonade. Let people decide which table they prefer.

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    I downvoted because the "grievance studies" outrage is a load of nonsense. People dedicating thousands of hours to making things up and getting a small fraction of them accepted for publication is evidence that bad papers can get through peer review (which is true in every field), not that whole fields of academia are irredeemable. They don't even live up to their claims, see eg slate.com/technology/2018/10/…
    – llama
    Commented Oct 29, 2019 at 20:31
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    @llama Yes. And let's be clear that what the "grievance studies" people did is outright fraud. Fields that aren't perpetual targets for this kind of maliciousness never seem to have to put up with this crap. Commented Oct 29, 2019 at 21:21
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    The grievance studies claims are backed up by evidence. Please read the article and the collected evidence. They puplished in the journals with the highest impact factor and one paper they puplished was "Mein Kampf" by Adolf Hitler, Chapter 12, with "white men" for jews and "women" for "oppressed arian people". They had a controll group for serious sociology, where no paper came through. Please read the article and check the evidence before claiming it is nonsense. In the end over 90% of their papers were accepted, when they cited someone important and work backwards to prove your claim. Commented Oct 29, 2019 at 23:56
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    An important lesson from the grievance studies papers is: you can get your paper published (or at least accepted or asked for a resubmit) if: Your methodology is shady Your conclusion does not depend on your data Your sample size is tiny You can suggest unethical things like chaining children based on their skin color to the floor As long you: Cite important people in that field Start with an accepted [but maybe stupid] conclusion (progressive stack, let more black instead of white people talk) Work backwards, suggest something unethical and justify it with the accepted conclusion Commented Oct 30, 2019 at 0:11
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    @ElizabethHenning "Fields that aren't perpetual targets for this kind of maliciousness never seem to have to put up with this crap." Because they're legitimate fields, not ideological nonsense.
    – user76284
    Commented Oct 30, 2019 at 23:08

Adding to what @xLeitix said, and from a curricular/class activities and program standpoint:

  • Add a class regarding BIAS in data with plenty of examples of GIGO (Garbage in- Garbage Out).
  • On the grading, force part of the grade to correspond to BIAS/exclusion testing for all homework/projects, and to consider the potential economic cost of such bias.
  • Assign works that are targeted to a different sample of people than the average majority. VG: Model how a daycare works rather than a futbol match.
  • Include mandatory books and lectures like: 'Invisible Women: Exposing Data Bias in a World Designed for Men‎', 'Coders: The Making of a New Tribe and the Remaking of the World' , Technically Wrong: Sexist Apps, Biased Algorithms, and Other Threats of Toxic Tech, 'When Cultures Collide: Leading Across Cultures'
  • Be open with times and regarding remote work.
  • Be strict regarding noninclusive behavior.

A lot of noninclusive behavior can be traced to the lack of knowledge about it or the comprehension of it's importance. Anything tech however is tricky, because for IT people (my field), social or political reasons are completely useless, you need to give rational reasons to why this matters, and the best and easiest way is to pinpoint economic or productivity reasons. If it seems that in small quantities its not much, then escalate to a country or society levels, even species/world level. With this you can ensure that the reasoning behind the curricula is sound and will be headed.

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    The suggestions are both vague and heavy-handed. Undergraduates are already being stressed out with lots of requirements, many of them vaguely relevant. If everyone in a CS program was striving for responsibility-heavy positions involving big data, your suggestions would have some validity; but otherwise, an extra class inserted into a CS program just "for social justice" would only antagonize most students. (Also, the titles of some of the books you mentioned don't quite sound even-handed -- albeit this seems to be a general problem with titles that does not always reflect on the books.) Commented Oct 28, 2019 at 23:18
  • Comments are not for extended discussion; this conversation has been moved to chat. I'll keep one comment that summarizes some of the main discussion points.
    – cag51
    Commented Nov 7, 2019 at 16:43

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