{"id":1140745,"date":"2023-05-18T07:31:01","date_gmt":"2023-05-18T11:31:01","guid":{"rendered":"https:\/\/www.prime-wow.com\/?p=1140745"},"modified":"2023-05-18T07:31:01","modified_gmt":"2023-05-18T11:31:01","slug":"introducing-data-science-concepts-and-skills-to-primary-school-learners","status":"publish","type":"post","link":"https:\/\/www.prime-wow.com\/?p=1140745","title":{"rendered":"Introducing data science concepts and skills to primary school learners"},"content":{"rendered":"

Every day, most of us both consume and create data. For example, we interpret data from weather forecasts to predict our chances of a good weather for a special occasion, and we create data as our carbon footprint leaves a trail of energy consumption information behind us. Data is important in our lives, and countries around the world are expanding their school curricula to teach the knowledge and skills required to work with data, including at primary (K\u20135) level.<\/p>\n

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\"Kate
Kate Farrell<\/figcaption><\/figure>\n
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Prof. Judy Robertson<\/figcaption><\/figure>\n<\/figure>\n
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In our most recent research seminar<\/a>, attendees heard about a research-based initiative called Data Education in Schools<\/a>. The speakers, Kate Farrell<\/a> and Professor Judy Robertson<\/a> from the University of Edinburgh, Scotland, shared how this project aims to empower learners to develop data literacy skills and succeed in a data-driven world.<\/p>\n

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\u201cData literacy is the ability to ask questions, collect, analyse, interpret and communicate stories about data.\u201d<\/p>\n

\u2013 Kate Farrell & Prof. Judy Robertson<\/cite><\/p><\/blockquote>\n

Being a data citizen<\/h2>\n

Scotland\u2019s national curriculum does not explicitly mention data literacy, but the topic is embedded in many subjects such as Maths, English, Technologies, and Social Studies. Teachers in Scotland, particularly in primary schools, have the flexibility to deliver learning in an interdisciplinary way through project-based learning. Therefore, the team behind Data Education in Schools developed a set of cross-curricular data literacy projects. Educators and education policy makers in other countries who are looking to integrate computing topics with other subjects may also be interested in this approach.<\/p>\n

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\"Becoming
Data citizens have skills they need to thrive in a world shaped by digital technology.<\/figcaption><\/figure>\n<\/div>\n

The Data Education in Schools projects are aimed not just at giving learners skills they may need for future jobs, but also at equipping them as data citizens in today\u2019s world. A data citizen can think critically, interpret data, and share insights with others to effect change.<\/p>\n

Kate and Judy shared an example of data citizenship from a project they had worked on with a primary school. The learners gathered data about how much plastic waste was being generated in their canteen. They created a data visualisation in the form of a giant graph of types of rubbish on the canteen floor and presented this to their local council.<\/p>\n

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\"A
Sorting food waste from lunch by type of material<\/figcaption><\/figure>\n<\/div>\n

As a result, the council made changes that reduced the amount of plastic used in the canteen. This shows how data citizens are able to communicate insights from data to influence decisions.<\/p>\n

A cycle for data literacy projects<\/h2>\n

Across its projects, the Data Education in Schools initiative uses a problem-solving cycle called the PPDAC cycle<\/a>. This cycle is a useful tool for creating educational resources and for teaching, as you can use it to structure resources, and to concentrate on areas to develop learner skills.<\/p>\n

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\"The
The PPDAC data problem-solving cycle<\/figcaption><\/figure>\n<\/div>\n

The five stages of the cycle are:\u00a0<\/p>\n

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  1. Problem<\/strong><\/a>: Identifying the problem or question to be answered<\/li>\n
  2. Pla<\/a><\/strong>n<\/strong><\/a>: Deciding what data to collect or use to answer the question<\/li>\n
  3. Data<\/strong><\/a>: Collecting the data and storing it securely<\/li>\n
  4. Analysis<\/strong><\/a>: Preparing, modelling, and visualising the data, e.g. in a graph or pictogram<\/li>\n
  5. Conclusion<\/strong><\/a>: Reviewing what has been learned about the problem and communicating this with others\u00a0<\/li>\n<\/ol>\n

    Smaller data literacy projects may focus on one or two stages within the cycle so learners can develop specific skills or build on previous learning. A large project usually includes all five stages, and sometimes involves moving backwards \u2014 for example, to refine the problem \u2014 as well as forwards.<\/p>\n

    Data literacy for primary school learners<\/h2>\n

    At primary school, the aim of data literacy projects is to give learners an intuitive grasp of what data looks like and how to make sense of graphs and tables. Our speakers gave some great examples of playful approaches to data. This can be helpful because younger learners may benefit from working with tangible objects, e.g. LEGO bricks, which can be sorted by their characteristics. Kate and Judy told us about one learner who collected data about their clothes and drew the results in the form of clothes on a washing line \u2014 a great example of how tangible objects also inspire young people\u2019s creativity.<\/p>\n

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    \"In<\/figure>\n<\/div>\n

    As learners get older, they can begin to work with digital data, including data they collect themselves using physical computing devices such as BBC micro:bit<\/a> microcontrollers or Raspberry Pi<\/a> computers.<\/p>\n

    Coming soon: the recording of Kate\u2019s and Judy\u2019s seminar for you to watch.<\/strong> You can access their slides here<\/a>.<\/p>\n

    Free resources for primary (and secondary) schools<\/h2>\n

    For many attendees, one of the highlights of the seminar was seeing the range of high-quality teaching resources for learners aged 3\u201318 that are part of the Data Education in Schools<\/a> project. These include:\u00a0<\/p>\n