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    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.

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    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.


    Item Type: Article
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Research Centres and Institutes: Birkbeck Knowledge Lab, Data Analytics, Birkbeck Institute for
    Depositing User: Dell Zhang
    Date Deposited: 26 Jan 2017 13:22
    Last Modified: 12 Jun 2021 12:44


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