BIROn - Birkbeck Institutional Research Online

    Predictive modelling of evidence informed teaching

    Zhang, Dell and Brown, C. (2017) Predictive modelling of evidence informed teaching. Archives of Data Science Series A 2 (1), ISSN 2363-9881.

    [img]
    Preview
    Text
    eit_long.pdf - Published Version of Record
    Available under License Creative Commons Attribution.

    Download (521kB) | Preview

    Abstract

    In this paper, we analyse the questionnaire survey data collected from 79 English primary schools about the situation of evidence informed teaching, where the evidences could come from research journals or conferences such as EDM. Specifically, we build a predictive model to see what external factors could help to close the gap between teachers' belief and behaviour in evidence informed teaching, which is the first of its kind to our knowledge. The major challenge, from the data mining perspective, is that the Likert scale responses are neither categorical nor metric, but actually ordinal, which requires special consideration when we apply statistical analysis or machine learning algorithms. Adapting Gradient Boosted Trees (GBT), we achieve a decent prediction accuracy (MAE=0.36) and gain new insights into possible interventions for promoting evidence informed teaching.

    Metadata

    Item Type: Article
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Research Centre: Birkbeck Knowledge Lab
    Depositing User: Dr Dell Zhang
    Date Deposited: 26 Jan 2017 13:22
    Last Modified: 26 Jun 2017 07:07
    URI: http://eprints.bbk.ac.uk/id/eprint/15998

    Statistics

    Downloads
    Activity Overview
    129Downloads
    135Hits

    Additional statistics are available via IRStats2.

    Archive Staff Only (login required)

    Edit/View Item Edit/View Item