How to teach data literacy: upgrade STEM to STEAM

Professor Sally Eaves on how we can enhance data literacy by embedding data skills across the ‘STEAM’ curriculum.

Professor Sally Eaves

CTO, founder and strategic advisor

Curiosity matters! And when it comes to teaching data literacy, it’s important to nurture a curiosity for data as early as possible, as this can help students build foundational skills – whether at home or in the classroom. To support this, teaching data concepts as a cross-curricular concept with relatable meaning, instead of in isolation, is critical for helping to connect learning with real world problems.

Data, afterall, is so much more than numbers within a chart or graph. Data means numbers in context. And it also means data stories.

This does not have to involve a complete overhaul of existing curriculums, but rather an evolution and pragmatic embedding of holistic data skill learning opportunities. Moving beyond teaching data literacy as a function of STEM – to one of STEAM – combining learning in Science, Technology, Engineering or Mathematics with learning in The Arts can become a key enabler to support exactly this – connecting beyond the numbers. 

Educators are already beginning to see enhanced accessibility, engagement and commitment levels when working with data through arts-based activities. This can range from the use of sports and video games as an approach to better relate to distributions, scatterplots and standard deviations, through to the use of arts supplies to create data sculptures and better understand data visualizations and how to find ‘the data story’ within. A fantastic example of this type of approach in action is the inspiring ‘EmPowered’ program which integrates STEAM principles in relation to the real-world issue of energy conservation. 

“A STEAM learning approach can bring data out of the textbook or confines of traditional curriculums across business and academia, and help to relocate and connect inquiry within the more accessible and meaningful realms of local, personal, everyday places, activities and events.”

Sally Eaves, ‘CEO Aspirational Futures’

Learning 4.0 – whose learning is it anyway? 

As well as what we’re teaching, it’s important to rethink and challenge assumptions about who we’re teaching today. A study by the University of Liverpool found that a significant proportion of young people – who are typically identified as ‘digital natives’ – actually have the weakest understanding of how their data is harvested online and used.

In addition, research [PDF] undertaken by Censuswide on behalf of Exasol, found that 54% of those aged between 16 and 21 were not familiar with the term ‘data literacy’, with only 43% considering themselves data literate. Perhaps unsurprisingly then, the same research found more than three quarters of young people believe they lack the practical skills they need for their future careers­ – especially in regards to decision-making, communication and problem-solving. 

These are issues that have only worsened during the pandemic. For example, one-in-seven children globally having missed more than three quarters of their in-person education (UNICEF 2020). And this ‘future readiness’ gap extends to adult learners too, with the recent ‘Human Impact of Data Literacy’ report [PDF] finding that, “only 16% of employees below a junior manager level felt fully prepared to use data effectively when entering their current role”.

With this in mind, it’s imperative to close these gaps in all age and experience groups. After all, data literacy is a foundational skill for all of us to be empowered citizens today and into our data-driven future. 

The ODI Data Skills Framework reflects the balance of skills that are needed. One side of the framework highlights the practical skills involved with working with data, the other focuses on the strategic and critical skills needed to interpret and understand the opportunities, challenges and impact of data from business through to society – and how to best navigate these.

In the middle, the role of the data ‘translator’ comes to the fore. This is the vital capacity to balance skills from each side and communicate with language that decision-makers from business to IT to the public can all understand. So, how can we better develop the holistic core competencies of data literacy?

Data literacy resources

From self-paced learning to knowledge sharing communities, the positive news is that there is increasing support available for individuals to become more data literate. A superb and highly collaborative example is the Data Literacy Project, which involves a number of technology and academic organizations making dedicated tools and discussion opportunities available more widely.

Specifically targeting the unemployed, the Step Program, is a free-to-access re-skilling and employment initiative designed to develop the vital data and analytics skills needed for in-demand jobs, helping to connect training to actual role placements. 

Additionally, organizational subject area communities and learning academies, such as Exasol, afford great value. The same can also be said for social media communities. Social media communities, which come together around shared interests, can sometimes be overlooked as a resource when in fact can actually provide another forum for learning, support, networking and mentoring.”

Exasol is also a national funding partner for STATWARS®, a competition which aims to get primary and secondary pupils excited about data by embedding it into the heart of the educational system, so they can more inherently develop a better understanding of its value.

Finally, the visibility of role models is hugely important to attract a diversity of experience into data-centric careers. A fantastic example of a program which endeavors to change the narrative on ‘what a tech career looks like’ is the 365 initiative run by Aspirational Futures. The initiative places a focus on inclusion and diversity every single day of the year. 

Recommendations for upskilling the next generation workforce 

Drawing on the above discussion, these recommendations integrate business and education opportunities to enhance both data literacy and skills confidence: 

  • Incorporate vocational job specific training alongside academic learning within curriculums.
  • Include data literacy training in all role disciplines – not just the ‘technical’ ones. 
  • Move beyond a STEM to STEAM focus to help foster holistic skill sets. This Includes supporting knowing ‘how to learn’ or metacognition which can help people learn ‘Smarter’ in a way that works best for them to optimize effectiveness and build confidence.
  • Make lifelong learning readily accessible and practical so employees can continue to hone their skills ‘on the job’. This can include project-based hackathon approaches that connects organizational values to the local community or wider societal challenges such as the Sustainable Development Goals (SDGs)
  • Create communities of practice around specific skills and interests to foster safe learning spaces and help move beyond mentoring to the sponsorship of others. 
  • Increase advocacy by Data Scientists and leading data science organizations, especially around promoting the democratization of skills accessibility, social data literacy and applying their ‘sphere of influence’ to scale data and social good outcomes.