One of the most important conversations in learning
Learners starting school this year will graduate into a jobs market very different from what we see today. We need a conversation about the implications for learning.
By 2030 the population of the US and the UK will be older. Emerging technologies like the Internet Of Things or 3D printing may have revolutionised supply chains and manufacturing, and more of us are predicted to live and work in cities.
Trends such as these have implications for the jobs, and skills, that will be in demand in 2030. The twin aims of this project, a partnership between Pearson, Nesta and Michael Osborne of the Oxford Martin School, is to generate quantified predictions of this future, and then to ask what that implies for learning.
The trends that will shape the future
The effect of automation is often the focus of predictions about the future of jobs. We wanted to extend this by cataloguing the wider set of trends that will shape the jobs market of 2030. Trends such as globalisation, demographic change, environmental sustainability, urbanisation, increasing inequality and political uncertainty.
We’ve also surfaced the uncertainties, the caveats and the way that these trends could interact. For example, although an ageing society could lead to increased health care spend, could technology deliver the step-changes in productivity that would alleviate these spending pressures?
We’ve brought together thought leaders to predict how these trends will affect the future demand for jobs
We’ve held foresight exercises in the USA and UK. Experts predicted the future demand for existing occupations
During the foresight exercise the Experts were reminded of the trends. They then mapped out how these trends will interact and affect different sectors of the economy. Eventually they were invited to predict the demand for jobs as currently defined - would they become more or less significant, and what confidence would they give to their prediction?
To inform their judgements, they had available ‘fact sheets’ that used official labor force data to specify the tasks and skills involved in each job, and, because the past is often a good guide to the future, historical data on the absolute number and share of the jobs market that the occupation has enjoyed over time.
We knew in advance the first set of jobs that we would present to the Experts, but the final two were selected by an active learning algorithm. We fed-in the Experts predictions, allowing the algorithm to identify the next set of jobs where it was asking for more human judgement.
In effect, Human and Machine Intelligence working hand-in-hand.
Identifying the skills, and skill combinations, that will be in demand
World-class foresight experts gathered to discuss and predict the likely demand for 60 existing occupations in the US and UK economies.
The experts considered how evolving technological, political, economic, and social factors may impact future trends in employment and education.
Their predictions are now being used to train a machine-learning algorithm, which will help us move from identifying in-demand jobs to in-demand skills.
The machine-learning algorithm is currently being deployed on US labor force O*NET data, a unique dataset which maps the skills and competencies needed to do roughly 1,000 jobs. The algorithm, developed by Michael Osborne and his team, will find patterns—maybe some unexpected—that no human ever could.
We expect to be able to share these insights by Spring 2017.
The implications for Learning
The conversation needs to be widened to include Educationalists as well as Economists. Parents as well as Policy Makers
Our ultimate hope is that this work provides a prompt to think about the future of learning.
For example, how can we efficiently and effectively re-skill adult learners so that they can thrive in this re-shaped jobs market? What subjects will be in demand at our Universities? Or, what does it mean for the pedagogies deployed in our classrooms? Should, for example, approaches like great group work be much more common?
These are important questions, but they are only a start as we move from insights and recommendations to the actions needed to realise these changes at scale.
We hope this important project contributes to that.