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Improving learning experience in MOOCs with educational content linking

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dc.contributor.advisor Victor W. Zue. en_US
dc.contributor.author Li, Shang-Wen, Ph. D. Massachusetts Institute of Technology en_US
dc.contributor.other Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. en_US
dc.date.accessioned 2017-05-11T19:59:24Z
dc.date.available 2017-05-11T19:59:24Z
dc.date.copyright 2017 en_US
dc.date.issued 2017 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/108989
dc.description Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017. en_US
dc.description Cataloged from PDF version of thesis. en_US
dc.description Includes bibliographical references (pages 153-163). en_US
dc.description.abstract Since the first MOOC (Massive Open Online Course) in 2011, there have been over 4,000 MOOCs 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 learner backgrounds make it difficult for instructors to interact with learners in a timely manner, which adversely affects their learning outcome. To address these challenges, in this thesis, we propose a framework of educational content linking. By linking pieces of learning content scattered in the various course materials into an easily accessible structure, we hypothesize that this framework will guide learners and improve content navigation. Since most instruction and knowledge acquisition in MOOCs takes place when learners are surveying course materials, better content navigation may help learners find supporting information to clear up confusion and improve the learning outcome. To support our conjecture, we present end-to-end studies to investigate our framework around two research questions. We first ask, does manually generated linking improve learning? To investigate this question, we choose two STEM courses, statistics and programming language, and demonstrate how the annotation of linking among course materials can be accomplished with collaboration between course staff and online workers. With this annotation, we implement an interface that can simultaneously present learning materials and visualize the linking among them. In a large-scale user study, we observe that this interface enables users to find desired course materials more efficiently, and retain more concepts more readily. This result supports the notion that manual linking does indeed improve learning outcomes. Second, we ask, can learning content be generated using machine learning methods? For this question, we propose an automatic 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 the proposed linking framework can be implemented at scale with machine learning techniques. en_US
dc.description.statementofresponsibility by Shang-Wen Li. en_US
dc.format.extent 163 pages en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582 en_US
dc.subject Electrical Engineering and Computer Science. en_US
dc.title Improving learning experience in MOOCs with educational content linking en_US
dc.title.alternative Improving learning experience in Massive Open Online Courses with educational content linking en_US
dc.type Thesis en_US
dc.description.degree Ph. D. en_US
dc.contributor.department Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. en_US
dc.identifier.oclc 986521772 en_US


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