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The challenges of open data: emerging technology to support learner journeys


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This presentation describes work being undertaken in a project funded by UKCES to use open data to support people in making decisions about future career choices

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The challenges of open data: emerging technology to support learner journeys

  1. 1. The challenges of open data: emerging technology to support learner journeys Graham Attwell, Pontydysgu Jenny Bimrose, IER University of Warwick PONTYDYSGU
  2. 2. LMI to support learner journeys What LMI do learners need? What do careers practitioners need to know? Where can relevant LMI be found? How do we know it’s high quality? Is LMI fit for purpose?
  3. 3. LMI for All To develop a careers LMI data tool that supports individuals make better decisions about learning and work  requiring access to, and use of, core national data sources
  4. 4. Funded by: Developed by consortia: PONTYDYSGU
  5. 5. Objectives  IDENTIFY: robust sources of LMI to inform decisions about learning and work  COLLATE: sources in an automated, single, accessible location for careers  FACILITATE: easy use of LMI data in conjunction with other data sources  EVALUATE: relevance of the data tool with key stakeholders
  6. 6. Process of developing the LMI database  Create a repository of careers labour market information  Take lessons learnt from pilot  Assess data sources in terms of usefulness for careers and accessibility  Link up data sources using a common structure  Test out database during hack and modding days  To open data up for multiple interfaces for a range of users
  7. 7. Process of development: Dynamic/Iterative Data Accessibility & Open Data Stakeholders & Communication
  8. 8. Overview of data sources  Office for National Statistics – ASHE, LFS, Census of Population, BRES  UKCES – Working Futures and ESS data  BIS, DfE, & other bodies – access to data on education and training courses  DWP - access to detailed data on vacancies by occupation  Devolved nations – statistical agencies  Eurostat, Cedefop, etc. – access to European data  Other data providers
  9. 9. Overview of data and indicators  Employment: historical, projected and replacement demand (Working Futures based on LFS, BRES)  Pay and earnings (estimates based on ASHE and LFS)  Hours (ASHE)  Unemployment rates (LFS)  Number of vacancies (ESS)  Occupational descriptions (ONS)  Skills, abilities and interests (O*NET)  Current vacancies (fuzzy search)  Higher education destinations (HESA)
  10. 10. Employment levels by occupation How many jobs are there? How many in my area? What are the past trends? What are likely future trends? Labour Force Survey, Working Futures Average earnings by occupation How much do people get paid for this job? How much at the start of their career? How much in my area? Annual Survey of Hours and Earnings Unemployment by occupation What proportion of people in this occupation are currently out of work? Annual Population Survey Profile of qualification level by occupation What level of qualification do people have in this job and what am I likely to need? Labour Force Survey, Working Futures Vacancies by occupation How many vacancies are there for this job? What proportion are hard to fill? Employer Skills Survey The data big questions…
  11. 11. Challenges in linking LMI sources  Data from range of sources  Classification and descriptions of occupations – use of SOC  Use of Annual Survey of Hours and Earnings data  Local data and sample sizes  Forecasting data from Working Futures  Access to job vacancy and skills data  Need for education and skills opportunities data
  12. 12. Emerging technology  Open and linked data  Supporting multiple platforms and devices  Developing data cubes  Automatic data upload (ETS)  Facilitating and supporting third party developers (API)  Confidentiality and non disclosure
  13. 13. Lessons learned  Technology challenges: interfaces and tools for updating data; developing a data cube; support for mobile devices  Data challenges: various data sets; occupational classifications and descriptions; standardisation of data from different data sets; local data; sample sizes; future trends; vacancy data; education and training opportunities; exploitation of US O*NET
  14. 14. Tour of the api…
  15. 15. Over to you… Work on your own or in groups:  Explore the api  Draw up some possibilities for app or web developments using the LMI for All database Approximately 20 minutes
  16. 16. Things to consider…  What ‘big questions’ are you trying to answer?  Apps to address different audiences, their learning styles and data needs  What about data mashing?  Potential to use on different devices
  17. 17. The big challenges ahead…
  18. 18. Early developments…  Access has now been provided to selected developers  Purpose – to understand what creative applications will emerge  Two interfaces from early adopters are publicly available, others are working towards launch
  19. 19. New services for education and training?
  20. 20. Can be used along side other, qualitative information… for example, icould 2 1 Data alongside career videos to encourage and inspire young people to think broadly about their careers.
  21. 21. …For a wide range of audiences…for example, RCU data dashboard A summary dashboard presenting information on the current and future employment demand for UK occupations to link LMI to a curriculum strategy.
  22. 22. …In a variety of contexts…for example, Career Trax Temporary extension shows Career Trax embedded in the ‘Right Move’ website.
  23. 23. Next steps  Integration of more data, including: Short-term - Course data; Job vacancy data Long-term - More on skills & abilities (O*NET); European data  Modding day to develop marketable apps  Implementation of data cubes
  24. 24. How to get involved? Find out more Follow us on Twitter @lmiforall Become an early adopter Stay in contact! Final launch early summer 2015
  25. 25. For more information… Email: Further information: