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  • Discovering Prerequisite Relationships Among Learning Objects: A Coursera-Driven Approach

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    MedioPUB3259.pdf (105.1Kb)
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    Accepted Manuscript (AM)
    Author(s)
    De Medio, Carlo
    Gasparetti, Fabio
    Limongelli, Carla
    Lombardi, Matteo
    Marani, Alessandro
    Sciarrone, Filippo
    Temperini, Marco
    Griffith University Author(s)
    Lombardi, Matteo
    Marani, Alessandro
    Year published
    2016
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    Abstract
    In this work we address the problem of automatically finding prerequisite relations among learning materials in order to help instructional designers to speed up the course building process. Ours is a data-driven approach, where a (machine) learner is trained to classify predecessor/successor relationships, given two didactic materials in a textual form. As the training set we use the learning materials extracted from Coursera. A first evaluation shows promising results.In this work we address the problem of automatically finding prerequisite relations among learning materials in order to help instructional designers to speed up the course building process. Ours is a data-driven approach, where a (machine) learner is trained to classify predecessor/successor relationships, given two didactic materials in a textual form. As the training set we use the learning materials extracted from Coursera. A first evaluation shows promising results.
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    Conference Title
    Advances in Web-Based Learning: ICWL 2016
    DOI
    https://doi.org/10.1007/978-3-319-47440-3_29
    Copyright Statement
    © 2016 Springer International Publishing AG. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher.The original publication is available at www.springerlink.com.
    Subject
    Information and Computing Sciences not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/336792
    Collection
    • Conference outputs

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