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    A multi-granularity pattern-based sequence classification framework for educational data

    Jaber, M. and Wood, Peter and Papapetrou, Panagiotis and Gonzalez-Marcos, A. (2016) A multi-granularity pattern-based sequence classification framework for educational data. In: UNSPECIFIED (ed.) Data Science and Advanced Analytics (DSAA), 2016 IEEE International Conference on. New York, U.S.: IEEE Computer Society, pp. 370-378. ISBN 9781509052066.

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    Abstract

    In many application domains, such as education, sequences of events occurring over time need to be studied in order to understand the generative process behind these sequences, and hence classify new examples. In this paper, we propose a novel multi-granularity sequence lassification framework that generates features based on frequent patterns at multiple levels of time granularity. Feature selection techniques are applied to identify the most informative features that are then used to construct the classification model. We show the applicability and suitability of the proposed framework to the area of educational data mining by experimenting on an educational dataset collected from an asynchronous communication tool in which students interact to accomplish an underlying group project. The experimental results showed that our model can achieve competitive performance in detecting the students' roles in their corresponding projects, compared to a baseline similarity-based approach.

    Metadata

    Item Type: Book Section
    Additional Information: (c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
    Keyword(s) / Subject(s): Feature extraction, Data mining, Time series analysis, Hidden Markov models, Proteins, Training
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Research Centre: Birkbeck Knowledge Lab
    Depositing User: Peter Wood
    Date Deposited: 17 Feb 2017 09:22
    Last Modified: 17 Feb 2017 09:22
    URI: http://eprints.bbk.ac.uk/id/eprint/16122

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