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oapen-20.500.12657-892002024-04-03T02:24:41Z Chapter Capturing Lifelong Learning Data through International Surveys and Novel Innovative Methods Boeren, Ellen Lido, Catherine Big Data Innovative Methods International Governmental Organizations Quantitative Methods Survey Research thema EDItEUR::U Computing and Information Technology Previous literature has highlighted the predominant use of qualitative research methods within the field of adult education. While a wide range of opportunities to exploit and gather large scale quantitative data are available, these avenues remain underexplored. The aims of this chapter are twofold. First, it familiarises readers with a range of datasets gathered through international survey programmes managed by International Governmental Organisations. Examples include the European Commission’s Adult Education Survey, the OECD’s Programme for the International Assessment of Adult Competencies (PIAAC), UNESCO’s Literacy and Assessment Programme (LAMP) and the World Bank’s STEP Skills Measurement Programme. It links the existence of these survey programmes to a wider debate on the use of benchmarks and indicators underpinning data-driven policy approaches. Second, it discusses examples of the application of novel and innovative methods that have been used to capture lifelong learning data in real-world projects. It highlights the work undertaken by the University of Glasgow’s Urban Big Data Centre, and zooms in on research undertaken within the Integrated Multimedia City Data (iMCD) project. Its work is being discussed against wider developments in relation to the use of ‘big data’ in the social sciences. Throughout the chapter, we reference the limitations of large survey and innovative data work, such as issues relating to privacy and the difficulties in including hard-to-reach groups. We focus on cooperative work in interdisciplinary teams with colleagues from varying methodological backgrounds who can contribute to projects underpinned by triangulation to provide comprehensive answers to relevant research questions. 2024-04-02T15:49:25Z 2024-04-02T15:49:25Z 2023 chapter ONIX_20240402_9791221502534_169 2704-5781 9791221502534 https://library.oapen.org/handle/20.500.12657/89200 eng Studies on Adult Learning and Education application/pdf n/a 9791221502534_22.pdf https://books.fupress.com/doi/capitoli/979-12-215-0253-4_22 Firenze University Press 10.36253/979-12-215-0253-4.22 10.36253/979-12-215-0253-4.22 bf65d21a-78e5-4ba2-983a-dbfa90962870 9791221502534 17 13 Florence open access
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Previous literature has highlighted the predominant use of qualitative research methods within the field of adult education. While a wide range of opportunities to exploit and gather large scale quantitative data are available, these avenues remain underexplored. The aims of this chapter are twofold. First, it familiarises readers with a range of datasets gathered through international survey programmes managed by International Governmental Organisations. Examples include the European Commission’s Adult Education Survey, the OECD’s Programme for the International Assessment of Adult Competencies (PIAAC), UNESCO’s Literacy and Assessment Programme (LAMP) and the World Bank’s STEP Skills Measurement Programme. It links the existence of these survey programmes to a wider debate on the use of benchmarks and indicators underpinning data-driven policy approaches. Second, it discusses examples of the application of novel and innovative methods that have been used to capture lifelong learning data in real-world projects. It highlights the work undertaken by the University of Glasgow’s Urban Big Data Centre, and zooms in on research undertaken within the Integrated Multimedia City Data (iMCD) project. Its work is being discussed against wider developments in relation to the use of ‘big data’ in the social sciences. Throughout the chapter, we reference the limitations of large survey and innovative data work, such as issues relating to privacy and the difficulties in including hard-to-reach groups. We focus on cooperative work in interdisciplinary teams with colleagues from varying methodological backgrounds who can contribute to projects underpinned by triangulation to provide comprehensive answers to relevant research questions.
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Firenze University Press
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2024
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https://books.fupress.com/doi/capitoli/979-12-215-0253-4_22
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1799945258128113664
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