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

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De Medio, Carlo
Gasparetti, Fabio
Limongelli, Carla
Lombardi, Matteo
Marani, Alessandro
Sciarrone, Filippo
Temperini, Marco
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2016
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Rome, Italy

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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.

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Advances in Web-Based Learning: ICWL 2016

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© 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.

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Information and Computing Sciences not elsewhere classified

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