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

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Author(s)
De Medio, Carlo
Gasparetti, Fabio
Limongelli, Carla
Lombardi, Matteo
Marani, Alessandro
Sciarrone, Filippo
Temperini, Marco
Year published
2016
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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
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