More Efficient Learning by Structuring, Classifying and Understanding Lectures in Online Courses
Date Issued
2016
Date
2016
Author(s)
Shen, Sheng-Syun
Abstract
The increasing popularity of Massive Open Online Courses (MOOCs) has resulted in a huge number of courses available over the Internet under various MOOCs platforms such as edX and Coursera. The wide variety and efficiency of such courses has offered great convenience to learners. However, it is still relatively inconvenient for some learners for lack of face-to-face interactions with lecturers in such platforms. Considering the above problem, this thesis aims at creating a set of new technologies for a comprehensive online learning platform, which could help users to learn more efficiently. This platform includes such functionalities as the following: structuring the lectures, within a course and across many courses, extracting the key term sets for better classifying the lectures, and machine understanding on the lectures. Lecture structuring includes not only the alignment between slide contents and spoken utterances within a course, but also providing the connections and prerequisite relationships between courses. Besides, automatically extracted key term sets for the lectures offer a better way for users to retrieve exactly the desired lectures. This thesis also conducts studies on machine understanding on the lectures, hoping to improve the learning scenario in the future.
Subjects
Massive Open Online Courses
Deep Learning
Natural Language Understanding
Support Vector Machine
Type
thesis
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