2: Good practices for teaching about data

When educating about data technologies – or fostering ‘critical data literacy’ – our research and practical experience have identified a number of good practices.

Course Section

Building on our experience and research, in this chapter we introduce a number of good practices when educating about data: what we found works well and what educators should pay attention to. The chapters after this then provide advice on how to identify 'good' educational resources on data, and on creating your own resources.

Teaching about Data 2: Good practices for teaching about data

1. Start with the students

Most likely, every person in a group of learners already has experiences with and assumptions about digital technologies. These are great entry-ways into a conversation. Invite their experiences as a starting point for an open dialogue about how these systems function, how they affect our lives and societies but also which shortcomings they have and which risks they entail.

Going on a journey of discovery together, relating to people’s own experiences, applying ‘learning by doing’ approaches and using interactive resources are approaches we found work well when educating about data.

Involve the learners on a personal level. We have found that this is a good way to navigate “issue fatigue”, the sense that people have that there are too many problematic issues that they could or should be concerned about. Demonstrate to learners that issues around data systems affect them personally. This could be done by relating to their own experiences with technology, but also by using real-life examples and case studies (see some examples here and here). Also stories – either real or fictional – are a good way to reach people, to communicate complex issues, foster critical thinking and to promote new narratives. Many of the resources we recommend in chapters 3 and 4 use a story-based approach and there are also online collections of real-life examples, for example of the ways data systems have led to harm.

2. Stay positive

  • Finding: Experts we interviewed emphasised “not to be moral” and tell learners “that’s wrong or that’s good”, but rather to empower them to “form your own opinion and make it heard”.

  • Teaching suggestion: We recommend refraining from overly negative or even fear-mongering approaches when educating about these issues. This approach is sometimes used by educational resources about the data tech in order to get people’s attention and emphasise the severity of the issues.

  • Counter Finding: Research has even found that some learners want to be “scare[d] into” caring about their data.

Based on our experiences and our research interviews with experts, we believe that fear-based approaches are, as one expert put it, "the worst way to learn because if you scare people, they stop learning". Equally, if learners do not resonate with that fear, their interest in learning may stop there too. Instead, we urge educators to take positive, motivational approaches and aim to make learning – also about critical issues – fun.

Clearly not everything covered within critical data literacy is warm and fuzzy. You can engage the challenging nature of these issues by fostering people’s imagination of different data futures. A number of educational resources about data systems only focus on how these technologies work, how they affect our societies and which risks they entail. While such critical understanding and reflection is essential, learning may be better served through future gazing.

  • Teaching suggestion: Combine critical perspectives with constructive considerations and imaginations. Asking questions such as ‘What kind of data future do we want and what steps do we need to take to get there?’ is a simple but effective way to motivate learners and avoid them from obtaining overly pessimistic future prospects when learning about data systems and their risks.

3. Prevent and combat resignation

As outlined in our first chapter, some people feel resigned, or have ‘given up’ on their data, because they feel powerless to stop the collection of their data.

In fact, the more they learn about the world the more this resignation may arise. In our research we found that this occurs as people learn more about data technologies, when they realise the ubiquitous collection of data in today’s societies, and when they find out about the many ways their data is used without their knowledge. Particularly people’s limited agency to stop these practices presents a great risk for resignation (and sometimes anger!).

  • Teaching suggestion: We strongly recommend including constructive advice when teaching about critical issues around data. One way to do this is to demonstrate to learners some steps they can undertake to better protect their data, for example by changing settings, using tracking blockers or switching to privacy-sensitive alternative services or add-ons.

In our experience, giving people insight into tools and methods empowers them and fosters confidence. This helps, but only to a limit. When you get into the details of critical data literacy, learners quickly realise that these are only superficial solutions, and they also entail a shift of responsibility to individuals.

Most problematic issues around data technologies are systemic issues and may also involve secretive practices. These require systemic solutions, such as more oversight, transparency and control. Individuals cannot solve these larger issues on their own and should not feel like this is their responsibility.

Consequently, another approach to providing constructive advice can be to encourage learners to assert their rights. They can make their voices heard in public debates and to actively shape their societies.

They can assert their rights as well. Concrete steps can include enabling their right of access and requesting a company disclose to them the data it holds about them, to get involved in civil society, to write to representatives or simply to spread the word and talk to others about their concerns regarding data systems.

See previous chapter. Go to next chapter.