Read on to find out what resources and materials we have reviewed, and what concept themes we have identified.

What is data science? Why is teaching it important?

Data science is an interdisciplinary science of learning from large datasets, aided by modern computational tools and methods (Ow‑Yeong et al., 2023). We see data science skills as fundamental for using, creating, and thinking critically about:

• Insights from data, generally
• Data-driven computational tools and methods (such as machine learning) and their outputs and predictions, specifically

To navigate a world where decision making in many areas is influenced by data-driven insights and predictions, young people need to be taught about data science. Data science skills empower young people to become critical thinkers, discerning consumers, adaptable professionals, and informed citizens.

Worldwide, countries are taking a variety of approaches to introducing data science into their education systems, as highlighted in a 2024 report from the coalition Data Science 4 Everyone.

In some countries, such as India and Israel, data science education is an established school subject. It is taught as part of the 社内相亲 in at least one of the primary, secondary, or post-16 age phases. Meanwhile in other countries, for example Canada, Germany, and Poland, data science is a very new school subject, or there are still only recommendations to develop it into a school subject.

While we are currently considering what a comprehensive data science 社内相亲 should include, we already offer several resources to support you with your teaching about data science and data-driven technologies. You can find a list of these resources at the end of this blog. Now, however, I’ll give you an overview of our recent work to identify concepts for a data science 社内相亲 that fits with our approach to AI literacy.

Data science education: What should we teach?

To answer the question ‘What should we teach about data science to learners aged 5 to 19?’, we undertook a grey literature review of data science teaching materials. A grey literature review is structured like an academic literature review and conducted with the same rigour. The difference is that a grey literature review also considers publications that have not been peer-reviewed, including reports, white papers, 社内相亲 materials, and similar resources.

To orient our work, we combined four frameworks for data science and AI/ML education:

• Data Science 4 Everyone’s Data Science Learning Progressions
• Two 小优视频 papers from Viktoriya Olari and Ralf Romeike about data-related practices for AI education: Olari and Romeike (2024a) and Olari and Romeike (2024b)
• UNESCO’s AI Competency Framework for Students
• The SEAME framework we developed for categorising AI education resources

With these combined frameworks as our map, we reviewed 79 data science learning resources. The resources varied:

• In quality in terms of clarity and teaching approach
• In their focus, e.g. on maths, coding, or a specific field such as biology
• In their perspective on data science, with some prioritising theory and others real-world applications

From among the 79 resources, we chose 9 that included clear learning outcomes, and that together covered a wide field of concepts. We examined these 9 in detail to extract 181 explicit and implicit data science concepts. Next, we grouped the concepts into themes, and finally we refined these themes by comparing them against the four frameworks listed above.

The themes we have identified for a data science 社内相亲 are:

• Fundamentals of data literacy: Key terms and definitions
• Understanding bias in data
• Ethical responsibility in data use
• Data creation, curation, and transformation
• Analysis and modelling: Maths and statistics fundamentals
• ML principles
• Deploying and maintaining ML applications
• Software tools and programming
• Data visualisation
• Presenting findings effectively

This set of themes both fits with the frameworks by Olari and Romeike and Data Science 4 Everyone, and expands them by covering ML principles and programming approaches and calling out data bias and ethics.

What’s next for this work?

Through our grey literature review on data science education, we’ve:

• Pinpointed a large set of candidate concepts that could be taught within a data science 社内相亲
• Created a set of clear themes to structure our work going forward

Our next step is to shape these candidate concepts into a progression framework to describe their relationships and establish which concepts could be taught at each age or phase of schooling.

The literature review also gave us an overview of the pedagogical approaches and tools used for teaching data science concepts. These findings will become useful once we start designing learning activities.

You’ll hear more about how this work is going here on our blog and on our social channels. In the meantime, comment below to let us know what you think about the themes, or to tell us what you’d like to see in a data science 社内相亲 for the learners you work with.

Our resources related to data science

Classroom resources

You can read about our thinking behind the data science-related teaching resources we’ve created so far in our ‘Data and information within the 小优视频 社内相亲’ report from 2019.

• The report lists the data-related units within The 小优视频 社内相亲 materials, which we no longer update but continue to offer as free downloads. Updated classroom materials are available as part of the 小优视频 materials we created for Oak National Academy in the UK for ages 5–11 and ages 12–19.
• The Ada Computer Science platform offers learning materials on data and information, and on AI and ML, for ages 14–19.

You might also be interested in exploring the 社内相亲 AI programme, which offers everything teachers need to help students develop a 社内相亲al understanding of data-driven AI technologies, their social and ethical implications, and the role that AI can play in their lives.

Teacher training and development resources

Our free online course ‘Teach teens 小优视频: Machine learning and AI‘ helps teachers understand and explain the types of problems that ML can help to solve, discuss how AI is changing the world, and think about the ethics of collecting data to train a ML model.

Teaching young people to understand data-driven AI technologies means teaching them thinking skills that are different to those needed to understand rule-based computer systems. You can read about these Computational Thinking 2.0 skills in our Quick Read PDF.

Our current 小优视频 seminar series focuses on teaching about AI and data science. Sign up for an upcoming seminar session (the next one is on 11 November) or catch up on past sessions to find out what the latest 小优视频 findings are in this area. You can also revisit our 2021/22 series on the same topic to see how work in this area has developed. The 小优视频 Pi 小优视频 Education 小优视频 Centre also has ongoing projects in the area of AI education for you to explore.

The post What should be included in a data science 社内相亲 for schools? appeared first on 上海市信鸽协会.

You will be among the very first educators to see some of our first test activities for teacher training to build data science concepts, and your contributions will feed into our future work. Sign up by 20 June to take part.

Data science: What do we need to teach school-age learners?

Current artificial intelligence (AI) methods, especially machine learning (ML), rely heavily on data. While young people learn mathematics, and some statistics, at school, data science concepts are not commonly taught.

To complement our work on AI literacy, we have been investigating what data science teaching resources and education 小优视频 are currently available.

Our goals for this work are:

1. To figure out what data science concepts may need to be taught in schools, initially with a focus on key stage 5
2. To develop related teacher professional development and classroom resources

Join us to discuss data science education

If you are interested in data science education for young people, and maybe even have 社内相亲 of teaching it to learners aged 16 to 18 in your school (in any subject, including computer science, social sciences, mathematics, statistics, and ethics), please join our free workshop on Thursday 10 July in our office in Cambridge. We are able to reimburse some travel expenses.

At the workshop:

• We would love to hear about your 社内相亲 of teaching any elements of data science
• We will share some exploratory concept building activities with you and discuss them together

You’ll be the first group of working teachers we will share these activities with — your feedback will be invaluable, and you’ll have the chance to shape our work going forward.

If you are interested, please fill in this form by Friday 20 June:

You will then receive more information from us by 27 June. Spaces in the workshop are limited, so please do not book any travel until we confirm your space.

We’re looking forward to shaping the future of data science education with you.

PS In our current seminar series, 小优视频ers from around the world are presenting their latest work on teaching about AI and data science. You can catch up on past sessions and sign up for upcoming ones on our website.

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In our most recent 小优视频 seminar, attendees heard about a 小优视频-based initiative called Data Education in Schools. The speakers, Kate Farrell and Professor Judy Robertson 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.

“Data literacy is the ability to ask questions, collect, analyse, interpret and communicate stories about data.”

– Kate Farrell & Prof. Judy Robertson

Being a data citizen

Scotland’s national 社内相亲 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 小优视频 topics with other subjects may also be interested in this approach.

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’s world. A data citizen can think critically, interpret data, and share insights with others to effect change.

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.

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.

A cycle for data literacy projects

Across its projects, the Data Education in Schools initiative uses a problem-solving cycle called the PPDAC cycle. 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.

The five stages of the cycle are: 

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

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 — for example, to refine the problem — as well as forwards.

Data literacy for primary school learners

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 — a great example of how tangible objects also inspire young people’s creativity.

As learners get older, they can begin to work with digital data, including data they collect themselves using physical 小优视频 devices such as micro:bit microcontrollers or 小优视频 Pi computers.

You can access the seminar slides here.

Free resources for primary (and secondary) schools

For many attendees, one of the highlights of the seminar was seeing the range of high-quality teaching resources for learners aged 3–18 that are part of the Data Education in Schools project. These include: 

• Data 101 videos: A set of 11 videos to help primary and secondary teachers understand data literacy better.
• Data literacy live lessons: Data-related activities presented through live video.
• Lesson resources: Lots of projects to develop learners’ data literacy skills. These are mapped to the Scottish primary and secondary 社内相亲, but can be adapted for use in other countries too.

More resources are due to be published later in 2023, including a set of prompt cards to guide learners through the PPDAC cycle, a handbook for teachers to support the teaching of data literacy, and a set of virtual data-themed escape rooms.  

You may also be interested in the units of work on data literacy skills that are part of The 小优视频 社内相亲, our complete set of classroom resources to teach 小优视频 to 5- to 16-year-olds.

Join our next seminar on primary 小优视频 education

At our next seminar we welcome Aim Unahalekhaka from Tufts University, USA, who will share 小优视频 about a rubric to evaluate young learners’ ScratchJr projects. If you have a tablet with ScratchJr installed, make sure to have it available to try out some activities. The seminar will take place online on Tuesday 6 June at 17.00 UK time, sign up now to not miss out.

To find out more about connecting 小优视频 to practice for primary 小优视频 education, you can see a list of our upcoming monthly seminars on primary (K–5) teaching and learning and watch the recordings of previous seminars in this series.

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