LIFELONG LEARNING

How can we use data to ensure that we have, and can continue to develop, the skills we need in the future

 

Challenge identifier: SC3-2017

 

Description

The market for education and learning is being heavily disrupted by new entrants using different business and pedagogical models. At the same time changes in the job market are creating the opportunities for new forms of education and learning, particularly due to the changes in work which will come as a result of automation and AI. Oxford economists believe that up to 40% of existing jobs will not exist in the next two decades and so it is more important than ever that our future workforce has the education, skills and training that will be required in the 21st century. In order to achieve this, instead of a pure focus on formal education a move towards more flexible and adaptable approaches that incorporates lifelong learning and development for people of all ages will need to be created.

Technology can help collect a vast amount of data on formal and informal learning and build tools to help prepare for the future needs of employers and the workforce. These include building predictive algorithms and personalised tools that help people of all ages. Data driven solutions may be able to help assess students level of knowledge enabling teachers to better plan and use their time more productively. In addition, data could help demonstrate the impact of these new, more informal education approaches by potentially aggregating data across learning platforms to demonstrate their effectiveness and certify individuals’ level of knowledge and skills.

We are particularly interested in solutions that leverage closed and shared data to:

  • Create predictive models for the skills needed in the near future and / or create models which help match individuals’ skills to jobs;
  • Identify ways for older people to continue to participate in the workforce by learning new skills;
  • Help educational institutions prepare and predict curricula;
  • Build data driven assessment tools that help teachers tailor teaching to students needs;
  • Personalise the learning experience for individuals based on their preferred method of learning;
  • Exploit and aggregate data used across different learning platforms; and
  • Aggregate, evaluate and certify informal learning such as DuoLingo, YouTube, Udemy etc.

Data

Examples of data include but are not limited to:

  • learning analytics
  • qualifications
  • employment history data (subject to data protection regulations)
  • general statistics about workforce, economic development and industry
  • collections of learning repositories
  • educational and training videos
  • data collected on private and public education, learning and training facilities and platforms.

 

Expected outcomes

Examples of outcomes may include but are not limited to:

  • new apps and services (web, mobile etc)
  • new algorithms (feature extraction, content analysis, prediction, matchmaking, content aggregation etc.)
  • new tools and processes to translate analytics insights into learning activities and education policies.

 

Expected impacts

Participants will need to provide details on how they would develop an impact measurement framework they would use to show how their solution:

  • helps people learn and gain new skills; or
  • give policy and education delivery organisations insight into their service provision and planning.

This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Grant Agreement 732506. Unless otherwise indicated, all materials created by the Data Pitch consortium are licensed under CC-BY 4.0