Annika Wolff works on our Education work stream team. She is also a Research Associate in the Maths, Computing and Technology Department at The Open University.
What is your role in MK:Smart?
The project as a whole is collecting a large range of data streams in the field of energy, water and transport and is looking into how we can ensure Milton Keynes is sustainable. What I’m doing, from the perspective of education, is trying to find ways of using this data and the research that’s being done around it, as teaching resources to teach big data skills in schools.
Why do students need to learn how to use data?
It’s crucial to get the next generation thinking about smart city topics, but also to ensure they have the skills to become future innovators with data. Beyond that, it’s currently becoming clear that there’s a general gap between what workforces need in terms of data skills and what people leave school with, so by teaching these skills we can make them more ready for the job market.
I also feel teaching data skills is very important because students are increasingly contributing data in daily life that is being used by a multitude of companies. Having more of an awareness of Big Data and what can be done with it means they will be able to make more informed decisions about what data they contribute when given a choice.
Which schools have you be engaging with?
I’ve been speaking to Milton Keynes schools as we are collecting local data, which obviously makes it more interesting to the teachers. They are also interested in the idea of using data that is very relevant to their students as it makes it more engaging. Teachers are certainly interested in the idea that this data is real. They are interested in how we can make it appropriate for the students, without resources becoming too abstract and losing their real context. Teachers tell me the kinds of comments they often receive are – ‘Why am I learning maths? What is it good for? What does this problem really tell me about my daily life?’, but as the resources we’re working with are being used in research projects and it’s Milton Keynes data, then they can answer these questions quite easily. In the future it will be interesting to see how these kinds of approaches could be scaled up and used in other schools outside of MK.
At what age do you recommend teaching data skills?
The age range I mainly work with is anywhere between 8-16 years old, although obviously each age group has its particular needs. When you look at the younger groups they have less curriculum constraints, and they tend to do things in a more unified fashion which is quite useful because the topics we are covering span across many different curricula subjects. In the primary school, obviously the level of understanding is lower than in the secondary schools, so there’s a challenge there in how to bring the topics and the concepts to life.
In the secondary schools, there can be conflicts with exams and a pressure on time. That said I’m going to both primary and secondary schools this year, so I will be engaging with all the key age ranges and finding out how the data can be tailored for each age group and what specific challenges teachers are facing.
So which subjects do data skills fit into?
It can depend on the age of the students. In a primary school I’m working in a maths class, and at Stantonbury Campus I’m working with a science group around energy. I’ve also had interest from a geography network in schools, who are particularly interested in the map-based visualisations. I’ve done some work in regular classroom sessions that are part of science, with high-ability groups who took part in a special session, but I’ve also received interest from schools about doing one day workshops.
What I’m trying to do now, as this is ultimately going to be used to teach online, is to try to get directly into the classroom and engage with different age groups and experiment with different modes of presenting this data. What I’ve found is that there is not just one appropriate curriculum subject, one appropriate delivery or one appropriate age range, as there is interest in using this data work in many different contexts. The challenge will be how to make resources that support a variety of contexts and can be used in a range of different scenarios.
Can you give us an example of how you get students engaged?
It’s about prompting students to ask questions of the data and think about how they can answer their own questions. I’ve developed a set of materials around home energy consumption, and I’m asking students to look at graphs and think about what is influencing the patterns of consumption over different days and between different households. They can almost tell stories of people’s daily lives through this data. I think this is a new way of thinking for students, as they can use the data as evidence for something, but also they would need to corroborate it by going out and finding some other source of evidence, for example speaking to the homeowner about their energy use.
What would be your predictions for smart city developments in the future?
I feel that the real innovation is going to come from the next generation who are starting to work with data at the school level. These students will become tomorrow’s entrepreneurs, as they’ll understand how to make data work for them much better than our generation does. Teaching data skills is vitally important in that regard.