THESIS DEFENSE: Improving Learning Experience in MOOCs with Educational Content Linking

Speaker: Shang-Wen (Daniel) Li , MIT CSAIL

Date: Thursday, December 08, 2016

Time: 3:00 PM to 4:00 PM Note: all times are in the Eastern Time Zone

Refreshments: 2:45 PM

Public: Yes

Location: 32-G449 (Stata Center - Patil/Kiva Conference Room)

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Host: Victor Zue, MIT CSAIL

Contact: Marcia G. Davidson, 617-253-3049, marcia@csail.mit.edu

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Reminders to: seminars@csail.mit.edu, sls-seminars@csail.mit.edu

Reminder Subject: TALK: THESIS DEFENSE: Improving Learning Experience in MOOCs with Educational Content Linking

Since its introduction in 2011, there have been over 4,000 MOOCs (Massive Open Online Courses) on various subjects on the Web, serving over 35 million learners. MOOCs have shown the ability to transcend time and space, democratize knowledge dissemination, and bring the best education in the world to every learner. However, the disparate distances between participants, the size of the learner population, and the heterogeneity of the learners' backgrounds make it extremely difficult for instructors to interact with the learners in a timely manner, and thus adversely affect learning experience and outcome.

To address the challenges, in this thesis, we propose a framework: educational content linking. By linking and organizing pieces of learning content scattered in various course materials into an easily accessible structure, we hypothesize that this framework can provide learners guidance and improve content navigation. Since most instruction and knowledge acquisition in MOOCs take place when learners are surveying course materials, better content navigation may help learners find supporting information to resolve their confusion and thus improves learning outcome and experience.

To support our conjecture, we present end-to-end studies to investigate our framework around two research questions. We first ask, can manually generated linking improve learning? For investigating this question, we choose two STEM courses, statistics and programming language, and demonstrate how the annotation of linking among course materials can be done with collaboration between course staff and online workers. With the annotation, we implement an interface that can present learning materials and visualize the linking among them simultaneously. We observer that, in a large-scaled user research, this interface enable users to search for desired course materials more efficiently, and retain more concepts more readily. This result supports the notion that manual linking can indeed improve learning outcomes. Second we ask, can learning content be generated with machine learning methods. For this question, we propose an automatic content linking algorithm based on conditional random fields. We demonstrate that automatically generated linking can still lead to better learning, although the magnitude of the improvement over the unlinked interface is smaller. We conclude that our linking framework can be implemented at scale with machine learning techniques.

Thesis Advisor: Victor Zue
Thesis Committee: Jim Glass and Rob Miller

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Created by Marcia G. Davidson Email at Tuesday, November 29, 2016 at 4:27 PM.