Contextualising data science

By Simon Johnson. Posted

With machinelearningforkids.co.uk, students can create their very own AI-powered bots like Lil Miquela

Simon Johnson shares the importance of contextualising data science topics to engage students and bring lessons to life

Data science is the study of data. It involves developing scientific methods, processes, and algorithms to extract knowledge from unstructured data in order to provide insights and support decision-making. In this article, I share the importance of bringing context to data-themed lessons — something that is important in all topics and subjects, but can be particularly helpful for what can occasionally be a dry topic.

Exploring data science in the classroom

When introducing pattern recognition, I often use the John Snow cholera map outlined in the previous article. While I find this activity is generally very well received by students, the activity does have its issues. The first problem I come across is one that I find with most unplugged lessons. Now don’t get me wrong; I’m a huge evangelist for the unplugged approach, but some students find it too abstract, and struggle to find a connection between the activity and the concepts being taught. The other problem is one suffered by most other subjects: that it has little context for the students. To paraphrase a student during one of my lessons: “Sir, why are you teaching us history in a computing lesson?”

Context is key to learning

The most common questions you will be asked by students when undertaking a new topic or subject are:

  • Why are we doing this? 

  • Will this help me with my exam? 

  • When will I ever use this? 

  • Will this help me get a job? 

In other words, how is it relevant? 

Contextualisation is not about changing the learning outcomes or objectives. It’s about modifying the learning materials so that they are relevant to the students. When teaching a new topic, such as data science, ask yourself the following questions: 

  • Does this relate to students’ aspirations or common interests?

  • Can it help students to understand how computing will be relevant to their future career choices?

  • Does it involve skills that can be used in the workplace? 

  • Can it be linked to popular culture or a recent newsworthy event?

Let’s now explore some of these questions. 

Does it relate to students’ interests?

When a topic connects with what students are interested in, engagement increases. However, it is prudent to understand the distinction between a student-centred approach, where students can explore their own interests, and a contextualised approach, in which the students are connected to a topic through a common interest.

Using a more student-centric approach can often lead to students focusing on the interest, rather than on the activity. Case in point: when creating a magazine cover as part of an image editing lesson, my students would often be preoccupied with finding pictures of the subject rather than focusing on the editing skills. However, I find that by connecting the learning to a common interest, engagement will still improve, but the opportunity for distraction will be reduced. Furthermore, I find that students engage better with authentic data they have collected or generated themselves, than with contrived examples that have no relevance to them. For example, a great way to introduce students to data analytics is to have them create infographics based on data they can collect about their own social media habits or mobile phone usage.

Does it involve skills that can be used in the workplace?

“When I grow up, I want to be a data scientist,” said no one ever! But, at its heart, data science is all about solving problems. The ability to think analytically and to recognise patterns are not just key computational thinking skills; they are also valuable skills for any workplace.

In a 2009 interview, Google’s chief economist, Hal Varian, stated, “The ability to take data — to be able to understand it, to process it, to extract value from it, to visualise it, to communicate it — that’s going to be a hugely important skill in the next decades.” Fast-forward to 2021 and many businesses would agree with Varian’s prediction — a sentiment echoed by LinkedIn’s 2020 Emerging Jobs Report, which found data science roles proliferating across nearly every industry.

And while some applications of data science, such as self-driving vehicles, might be fairly obvious, there are some less obvious examples that are very relatable and which you can use to draw students into the subject. For example, Liverpool Football Club’s recent record-breaking run owed a lot of its success to data science. The club had been collecting and analysing exhaustive amounts of data in order to dictate the style of play and determine who should play — all managed by a dedicated team of researchers, statisticians, and data scientists. 

Can it be linked to popular culture or a newsworthy event?

Data science plays an integral part in our everyday lives; it’s just not always obvious where it’s being applied! It’s no secret that companies such as Amazon, Facebook, Netflix, and Spotify all use AI, machine learning, and data science to interact with the end user to influence their content choices. What might be a surprise to students, though, is that the very same technology is used to create those infamous Tom Cruise deepfake videos (AI-generated fake videos) on TikTok, and it’s also behind the faces of popular AI influencers such as Lil Miquela!

Now, thanks to free AI teaching initiatives like machinelearningforkids.co.uk, students can create their own AI-powered projects, using Scratch or Python, while also exploring the power of big data. By explaining that these topics and tasks are building the skills that are behind technology that they engage with and enjoy, you are much less likely to hear, “Why are we learning this?”

By making computing relevant and providing a real-life context, you can create meaningful learning experiences for your students. So, what will you do to contextualise learning? 


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