In September, the Royal Society and STEM Learning held a workshop at the National STEM Learning Centre in York, Data skills for all. Engaging with education practitioners, it considered how the education system provides young people with data science knowledge and skills.
Data science has revolutionised how we understand the world and interact. In his welcome address, Dave Gibbs, Senior Computing and Technology Specialist at STEM Learning, highlighted the huge growth over recent years in the collection and availability of data, the critical importance of data analysis to breakthrough discoveries in science, and what these developments may mean for teachers and teaching.
Data skills across careers and professions
Professor Emma McCoy, Vice-Dean (Education) for the Faculty of Natural Sciences and Professor of Statistics in the Mathematics Department at Imperial College London, took us through some of the findings from the Royal Society’s Dynamics of Data Science report published in May, highlighting the magnitude of the rise in demand from employers for individuals with specialist data science skills. Overall, demand for Data Scientists and Advanced Analysts increased by 231% between 2013 and 2018. The report makes the case for more to be done to build data science knowledge and skills from school to degree level.
I was struck by Emma’s personal story of her route into statistics and data science, which highlighted the need to nurture young people’s interest in mathematics and statistics through inspiring examples of the application of data and experience of datasets in the curriculum. Emma made the salutary point that we currently fail to equip many individuals who enter professions such as teaching, journalism and politics with the knowledge and skills to reason effectively with data, and this is something that education simply must address.
Young people and the curriculum
My keynote talk considered what this might mean for education to age 18. In 2018 I reviewed the integration of foundational knowledge and skills for data science in the primary and secondary curriculum to age 16, concluding that, at these stages at least, many of the formal curriculum building blocks are in place – in mathematics, the sciences, computing and elsewhere. There are challenges in delivering change; schools could benefit from support to put coherent curricula and teaching in place. In 16-18 education the picture is different: young people need the opportunity to study a wider range of subjects to age 18 to offer proper scope to foster skills in reasoning and problem-solving with data.
The workshop heard the views of a panel of curriculum experts on what we want to achieve and how we can get there:
- Stella Dudzic, Programme Leader (Curriculum and Resources), MEI, shared her knowledge of working with schools and teachers to implement the new A level Mathematics requirement to use a large data set (LDS) and to introduce new AS-equivalent ‘Core Maths’ qualifications for students not studying A level Mathematics. There are practical challenges to upskill teachers of mathematics to teach a version of statistics that is not about plugging numbers into formulae but reasoning with data, drawing conclusions and using technology in the process. The MEI-led Advanced Maths Support Programme (AMSP) is there to help.
Kate Farrell, Director of Curriculum Development, University of Edinburgh, Data Education in Schools project told us about moves in Scotland to develop a practical interdisciplinary data education curriculum, mapped closely to the existing mathematics and computing curriculum. Five local authorities, local further education colleges, Edinburgh Napier and Heriot Watt universities and local employers are working together to offer opportunities to students at all stages to develop digital and data skills.
Miles Berry, Principal lecturer and the subject leader for Computing Education at the University of Roehampton said that there are many opportunities for the Computing curriculum in England to expose students to data, but this is not happening as it should. He argued for a framework of foundations/application/implications to promote a broad and balanced approach to encompassing data, starting with ‘old school probability and statistics’, moving to the application of tools and techniques then on to considering implications such as data privacy and use.
Neil Sheldon, Curriculum Lead (UK) for the International Data Science in Schools Project (IDSSP) told us about development, by a collaboration of experts internationally, of a data science curriculum for 16 to 18 year-olds. Roughly equivalent in size to an A level, the aims of the curriculum are two-fold: to ensure that students build an understanding of how data can be acquired and used to make decisions, and to instil interest and enthusiasm for data science as a specialism and potential future career. The project has developed accompanying resources and a curriculum framework for teacher development.
Workshop attendees considered practical interventions and ways forward for education to meet the challenge of data skills for all. We covered six fundamental questions regarding data science education, focusing on primary, secondary and other aspects of education including parents, teachers, and older learners: How can known constraints be overcome to achieve data skills for all? How can we make sure that data skills are taught cross-curriculum and who should take the lead? What can/should other organisations do? What are the highest priority issues and how can these be overcome? How can data literacy in the home be promoted? And what are the basic elements that need to be incorporated into lessons? A short summary note of the workshop, including these discussions and their next steps, has just been published.
A key theme that emerged is the difficulty for busy teachers to work across curriculum areas. We should not expect them to, but we can help towards a more meaningful and coherent experience for students in the use and analysis of data (for example across mathematics, the sciences and computing) underpinned by systematic detailed curriculum design. STEM Learning are now hosting a growing collection of free-to-access teaching resources supporting data skills; and the Royal Society will be leading on a programme of work investigating what data, computing or statistics mean for the future of education in mathematics.
There was a strong message that we need to invest seriously in upskilling teachers. We are not going to solve the need for curriculum opportunities for all in 16-18 education overnight, but workshop attendees agreed that this should remain firmly on the agenda. This is something the Royal Society will be working on over the coming year through its project Jobs are changing, so should education: they’ll be thinking about how to broaden and adapt our education system both to meet the needs of a technological economy, and to improve the wellbeing and civic participation of all members of our society.