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    Artificial intelligence and big data to close the achievement gap

    du Boulay, B. and Poulovassilis, Alexandra and Holmes, W. and Mavrikis, M. (2018) Artificial intelligence and big data to close the achievement gap. In: Luckin, R. (ed.) Enhancing Learning and Teaching with Technology: What the research says. UCL IoE Press. ISBN 9781782772262. (In Press)

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    Abstract

    We observe achievement gaps even in rich western countries, such as the UK, which in principle have the resources as well as the social and technical infrastructure to provide a better deal for all learners. The reasons for such gaps are complex and include the social and material poverty of some learners with their resulting other deficits, as well as failure by government to allocate sufficient resources to remedy the situation. On the supply side of the equation, a single teacher or university lecturer, even helped by a classroom assistant or tutorial assistant, cannot give each learner the kind of one-to-one attention that would really help to boost both their motivation and their attainment in ways that might mitigate the achievement gap. This chapter argues that we now have the technologies to assist both educators and learners, most commonly in science, technology, engineering and mathematics subjects (STEM), at least some of the time. We present case studies from the fields of Artificial Intelligence in Education (AIED) and Big Data. We look at how they can be used to provide personalised support for students and demonstrate that they are not designed to replace the teacher. In addition, we also describe tools for teachers to increase their awareness and, ultimately, free up time for them to provide nuanced, individualised support even in large cohorts.

    Metadata

    Item Type: Book Section
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Research Centre: Birkbeck Knowledge Lab
    Depositing User: Alexandra Poulovassilis
    Date Deposited: 29 Nov 2017 14:32
    Last Modified: 29 Nov 2017 14:32
    URI: http://eprints.bbk.ac.uk/id/eprint/20288

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