MOOC Analytics

Learner Modeling and Content Generation

Doctoral Thesis (2019)
Author(s)

Guanliang Chen (TU Delft - Web Information Systems)

Contributor(s)

G.J.P.M. Houben – Promotor (TU Delft - Web Information Systems)

C. Hauff – Copromotor (TU Delft - Web Information Systems)

Research Group
Web Information Systems
Copyright
© 2019 G. Chen
More Info
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Publication Year
2019
Language
English
Copyright
© 2019 G. Chen
Research Group
Web Information Systems
Bibliographical Note
SIKS Dissertation Series No. 2019-13@en
ISBN (print)
978-94-028-1482-8
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

Massive Open Online Courses (MOOCs), as one of the popular options for people to receive education and learn, are endowed with the mission to educate the world. Typically, there are two types of MOOC platforms: topic-agnostic and topic-specific. Topic-agnostic platforms such as edX and Coursera provide courses covering a wide range of topics, while topic-specific MOOC platforms such as Duolingo and Codeacademy focus on courses in one specific topic. To better support MOOC learners, many works have been proposed to investigate MOOC learning in the past decade. Still, there are many other aspects of MOOC learning to be explored.

In this thesis, we focused on (i) learner modeling and (ii) generation of educational material for both topic-agnostic and topic-specific MOOC platforms.

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