3: What makes a 'good' educational resource about data?
How do you find the right educational resource for your group of learners? We summarise key aspects to consider and recommend specific formats and resources.
Educators are keen to teach, learners are keen to learn. As we covered in previous chapters, the learning gaps are clear. Many citizens are unaware of the ways in which their data is used, data systems function and how they influence their lives and our societies. These aspects crucially need to be included when educating about digital technologies. But there’s another gap too: educators don’t know where to find teaching resources.
Teaching about Data 3: What is a ‘good’ educational resource about data?
- Finding: In our consultations with educators, we heard that they’re unhappy with their access to information and educational resources.
- Finding: In our research, we found that many educators feel better equipped to teach about digital technologies in general in contrast to topics around (big) data systems and algorithms. Nearly half of the surveyed educators were unhappy with material on these topics.
Fortunately, many educational resources on these topics already exist, although they seem to be not as well-known among educators. In chapter four, we will present a small selection of our favourite resources as well as an online database that collects such critical data literacy resources. In this chapter we provide some advice on selecting and using existing educational resources on data, based on our practical experiences and research.
Before we get started though, we would like to make clear: education about data and tech does not have to take place digitally. Although many of the resources we link to from this resource are also online resources, this is not a prerequisite for teaching about data. Critical data literacy can also be fostered through ‘traditional’ educational approaches, such as reading, discussion, thought experiments, analogue games, worksheets and posters.
1. Look for easily accessible and fun resources with helpful visuals
As already outlined in chapter two, we believe that learning – also about critical issues – should be fun. Regardless of whether the educational resource is analogue or digital, we recommend looking for easily accessible and entertaining resources. This is even more the case when considering the risk of resignation (see chapter 1).
- Teaching suggestion: we recommend refraining from negative or gloomy resources and instead selecting approachable, entertaining, colourful and playful material. Of course, what people experience as entertaining differs and educators should always consider which type of resource fits their particular audience.
- Finding: Our research as well as prior studies have found that interactive approaches are a great way to engage learners and that many learners prefer interactive resources when learning about data systems.
In practice, this can mean selecting an interactive online resource, but it can also mean deciding to use open and interactive teaching formats.
Especially since data issues are often complex and opaque, taking an interactive approach and making learning feel like a conversation or an experience in itself can help create engagement and personal involvement.
On top of being complex, data systems are also often immaterial. Visualisations can help learners grasp issues around data tech and make them more tangible. When looking for visualisations or selecting teaching material that uses visuals, we recommend avoiding stereotypical images (e.g., zeros and ones, the padlock, the matrix), and rather using visuals that people can relate to (e.g., the ‘data octopus’, your ‘data shadow’).
For some interactive and playful examples, see:
- How Normal Am I art project
- Radio Television Swiss's Datak video game
- Discriminator interactive documentary film
For some good visualisations, see:
2. No one-size-fits-all approach: Which is the right resource for your audience?
Different audiences need different approaches. Our own experiences and research as well as other scholars’ research have found that there is no one-size-fits-all approach to critical data literacy. Educators know their own audience best and should therefore consider which resources are best for which learners. Some characteristics of the audiences that can be considered as you review the available resources include: prior knowledge, digital skills, location, and if they belong to a vulnerable group that is disproportionately affected by the risks that come with data systems.
In our research, our expert interviewees highlighted that it is important to adapt one’s narrative to each audience. Which narratives does the audience already hold about data and how receptive are they for different messages? What interests them – what is their ‘hook’ (e.g., privacy, equality, discrimination, transparency, political fairness etc.)? Apart from these questions, also questions of representation should be considered. Particularly when resources use personas, visualisations of people or stories, we recommend considering if your audience could identify with or is represented in these.
Here are some examples for differing audiences:
- The London School of Economics and Political Science's Data and Privacy Toolkit for Young People (different sections for different audiences)
- Our Data Bodies Project's Digital Defense Playbook (pdf) (very considerate of adapting to different audiences in the individual exercises throughout the workbook)
- Center for Humane Technology (different sections for different audiences)
3. Check: Who created the resource? When?
Educators need to ask who created an educational resource and when. Some of the existing freely available material is created by groups with strong interests. Examples include resources on privacy by retailers of ‘secure technologies’, teaching material on digital media by a big telecommunications company, or resources from varying governments’ and their agencies. This does not mean that such resources are partisan, but some attention should be paid to the creation context of each resource.
For instance, industry may focus more on ‘personal responsibility’ and using the settings they provide you; rather than identifying the more secretive data sources they can access that people cannot control whether the companies have access or not. Some government content presumes that the audience is in their jurisdiction and in turn have legal rights that may not translate or reach across borders.
Apart from that, the landscape of digital and data technologies moves in an extremely fast pace and some education material dates quickly. In our experience, most online educational resources about data are not updated regularly. Therefore, it is important to pay attention to when a resource was first published. While an overall introduction to 'what is privacy' (like our own old video might not yet be outdated, a guide on how to change one’s social media settings to avoid data collection from the same year certainly will be.