Discovering prerequisite relationships among learning objects: A coursera-driven approach

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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 datadriven 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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De Medio, C., Gasparetti, F., Limongelli, C., Lombardi, M., Marani, A., Sciarrone, F., & Temperini, M. (2016). Discovering prerequisite relationships among learning objects: A coursera-driven approach. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10013 LNCS, pp. 261–265). Springer Verlag. https://doi.org/10.1007/978-3-319-47440-3_29

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