The digital divide: interactions between socioeconomic disadvantage and computing education

Digital technology is developing at pace, impacting us all. Most of us use screens and all kinds of computers much more than we did five years ago. The total number of apps downloaded globally each quarter has doubled since 2015, reflecting both increased smartphone penetration and the increasingly prominent role of apps in our lives. However, access to digital technology and the internet is not yet equal: there is still a ‘digital divide’, i.e. some people do not have as much access to digital technologies as others, if any at all.

This month we welcomed Dr Hayley Leonard and Thom Kunkeler at our research seminar series, to present findings on ‘Why the digital divide does not stop at access: understanding the complex interactions between socioeconomic disadvantage and computing education’. Both Hayley and Thom work as researchers at the Raspberry Pi Foundation, where we have a focus on increasing our understanding of computing education for all. They shared some results of a research project they’d carried out with a group of young people who benefitted from our Learn at Home campaign.

Digital inequality: beyond the dichotomy of access

Hayley introduced some of the existing research and thinking around digital inequality, and Thom presented the results of their research project. Setting the scene, Hayley explained that the term ‘digital divide’ can create a dichotomous have/have-not view of the world, as can the concept of a ‘gap’. However, the research presents a more nuanced picture. Rather than describing digital inequality as purely centred on access to technology, some researchers characterise three levels of the digital divide:

  • Level 1: Access
  • Level 2: Skills (digital skills, internet skills) and uses (what you do once you have access)
  • Level 3: Outcomes (what you achieve)

This characterisation is useful because it enables us to look beyond access and also towards what happens once people have access to technology. This is where our Learn At Home campaign came in.

The presenters gave a brief overview of the impact of the campaign, in which the Raspberry Pi Foundation has partnered with 80 youth and community organisations and to date, thanks to generous donors, has given 5100 Raspberry Pi desktop computer kits (including monitors, headphones, etc.) to young people in the UK who didn’t have the resources to buy their own computers.

Hayley Leonard presents an online slide describing the interview responses of recipients of Raspberry Pi desktop computer kits, which revolved around five themes: ease of homework completion; connecting with others; having their own device; new opportunities for learning; improved understanding of schoolwork.
Click on the image to enlarge it. Learn more in the first Learn at Home campaign impact report.

Computing, identity, and self-efficacy

As part of the Learn At Home campaign, Hayley and Thom conducted a pilot study of how young people from underserved communities feel about computing and their own digital skills. They interviewed and analysed responses of fifteen young people, who had received hardware through Learn At Home, about computing as a subject, their confidence with computing, stereotypes, and their future aspirations.

Thom Kunkeler presents an online slide describing the background and research question of the 'Learn at Home campaign' pilot study: underrepresentation, belonging, identity, archetypes, and the question "How do young people from underserved communities feel about computing and their own digital skills?".
Click on the image to enlarge it.

The notion of a ‘computer person’ was used in the interview questions, following work conducted by Billy Wong at the University of Reading, which found that young people experienced a difference between being a ‘computer person’ and ‘doing computing’. The study carried out by Hayley and Thom largely supports this finding. Thom described two major themes that emerged from their analysis: a mismatch between computing and interviewees’ own identities, and low self-indicated self-efficacy.

Showing that stereotypes still persist of what a ‘computer person’ is like, a 13-year-old female interviewee described them as “a bit smart. Very, very logical, because computers are very logical. Things like smart, clever, intelligent because computers are quite hard.” Four of the interviewees were also more likely to associate a ‘computer person’ with being male.

Thom Kunkeler presents an online slide of findings of the 'Learn at Home campaign' pilot study. The young people interviewed associated the term 'computing person' with the attributes smart, clever, intelligent, nerdy/geeky, problem-solving ability.
The young people interviewed associated a ‘computing person’ with the following characteristics: smart, clever, intelligent, nerdy/geeky, problem-solving ability. Click on the image to enlarge it.

The majority of the young people in the study said that they could be this ’computer person’. Even for those who did not see themselves working with computers in the future, being a ’computer person’ was still a possibility: One interviewee said, “I feel like maybe I’m quite good at using a computer. I know my way around. Yes, you never know. I could be, eventually.”

Five of the young people indicated relatively low self-efficacy in computing, and thought there were more barriers to becoming a computer person, for example needing to be better at mathematics. 

In terms of future career goals, only two (White male) participants in the study considered computing as a career, with one (White female) interviewee understanding that choosing computing as a qualification might be important for her future career. This aligns with research into computer science (CS) qualification choice at age 14 in England, explored in a previous seminar, which highlighted the interaction between income, gender, and ethnicity: White girls from lower-income families were more likely to choose a CS qualification than White girls more from more affluent families, while very few Asian, Black, and Chinese girls from low-income backgrounds chose a CS qualification.

Evaluating computing education opportunities using the CAPE framework

An interesting aspect of this seminar was how Hayley and Thom situated their work in the relatively new CAPE framework, which describes different levels at which to evaluate computer science education opportunities. The CAPE framework highlights that capacity and access to computing (C and A in the framework) are only part of the challenge of making computer science education equitable; students’ participation (P) in and experience (E) of computing are key factors in keeping them engaged longer-term.

A diagram illustrating the CAPE framework for assessing computing education opportunities according to four aspects. 1, capacity, which relates to availability of resources. 2, access, which relates to whether learners have the opportunity to engage in the subject. 3, participation, which relates to whether learners choose to engage with the subject. 4, experience, which relates to what the outcome of learners' participation is.
Socioeconomic status (SES) can affect learner engagement with computing education at four levels set out in the CAPE framework.

As we develop computing education in the curriculum, we can use the CAPE framework to evaluate our provision. For example, where I’m writing from in England, we have the capacity to teach computing through the availability of professional development training for teachers, fully developed curriculum materials such as the Teach Computing Curriculum, and community support for teachers through organisations such as Computing at School and the National Centre for Computing Education. In terms of access we have an established national curriculum in the subject, but access to it has been interrupted for many due to the coronavirus pandemic. In terms of participation we know that gender and economic status can impact whether young people choose computer science as an elective subject post-14, and taking an intersectional view reveals that the issue of participation is more complex than that. Finally, according to our seminar speakers, young people’s experience of computing education can be impacted by their digital or technological capital, by their self-efficacy, and by the relevance of the subject to their career aspirations and goals. This analysis really enhances our understanding of digital inequality, as it moves us away from the have/have-not language of the digital divide and starts to unpack the complexity of the impacting factors. 

Although this was not covered in this month’s seminar, I also want to draw out that the CAPE framework also supports our understanding of global computing education: we may need to focus on capacity building in order to create a foundation for the other levels. Lots to think about! 

If you’d like to find out more about this project, you can read the paper that relates to the research and the impact report of the early phases of the Learn At Home initiative

If you missed the seminar, you can find the presentation slides on our seminars page and watch the recording of the researchers’ talk:

Join our next seminar

The next seminar will be the final one in the current series focused diversity and inclusion, which we’re co-hosting with the Royal Academy of Engineering. It will take place on Tuesday 13 July at 17:00–18:30 BST / 12:00–13:30 EDT / 9:00–10:30 PDT / 18:00–19:30 CEST, and we’ll welcome Prof Ron Eglash, a prominent researcher in the area of ethnocomputing. The title of Ron’s seminar is Computing for generative justice: decolonizing the circular economy.

To join this free event, click below and sign up with your name and email address:

We’ll email you the link and instructions. See you there!

This was our 17th research seminar — you can find all the related blog posts here, and download the first volume of our seminar proceedings with contributions from previous guest speakers.

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“Computer person”.
It’s not a new or unusual thing. You get all sorts of stereotyped people, sports people, art people, music people, business people etc. These are not confined to any particular social group, and they can all be affected by access to resources. Why is “computer peopple” different?

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While the shortage of talented musicians and artists has also lowered to critical levels, I think the need for computer people is perceived as different than music or art people because of the economic and strategic relevance of computer skills and literacy as understood by government and corporate needs.

To avoid an entire society being plunged into financial ruin (and further reduce the ability to support careers in art and music), it appears necessary to provide opportunities for every able person to become skilled in using computers. As interest follows from ability, this should lead to greater productivity or at least enough knowledge to prevent current infrastructure from being destroyed by the cyber forces of hostile nations and criminals.

Reply to Eric Olson

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