Discovering Prerequisite Relationships Among Learning Objects: A Coursera-Driven Approach
File version
Accepted Manuscript (AM)
Author(s)
Gasparetti, Fabio
Limongelli, Carla
Lombardi, Matteo
Marani, Alessandro
Sciarrone, Filippo
Temperini, Marco
Griffith University Author(s)
Primary Supervisor
Other Supervisors
Editor(s)
Date
Size
File type(s)
Location
Rome, Italy
License
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.
Journal Title
Conference Title
Advances in Web-Based Learning: ICWL 2016
Book Title
Edition
Volume
Issue
Thesis Type
Degree Program
School
Publisher link
Patent number
Funder(s)
Grant identifier(s)
Rights Statement
Rights 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.
Item Access Status
Note
Access the data
Related item(s)
Subject
Information and Computing Sciences not elsewhere classified