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Since its introduction in 2011, there have been over 4000 MOOCs on varioussubjects on the Web, serving over 35 million learners. MOOCs have shown theability to democratize knowledge dissemination and bring the best education inthe world to every learner. However, the disparate distances betweenparticipants, the size of the learner population, and the heterogeneity of thelearners' backgrounds make it extremely difficult for instructors to interactwith the learners in a timely manner, which adversely affects learningexperience. To address the challenges, in this thesis, we propose a framework:educational content linking. By linking and organizing pieces of learningcontent scattered in various course materials into an easily accessiblestructure, we hypothesize that this framework can provide learners guidance andimprove content navigation. Since most instruction and knowledge acquisition inMOOCs takes place when learners are surveying course materials, better contentnavigation may help learners find supporting information to resolve theirconfusion and thus improve learning outcome and experience. To support ourconjecture, we present end-to-end studies to investigate our framework aroundtwo research questions: 1) can manually generated linking improve learning? 2)can learning content be generated with machine learning methods? For studyingthe first question, we built an interface that present learning materials andvisualize the linking among them simultaneously. We found the interface enablesusers to search for desired course materials more efficiently, and retain moreconcepts more readily. For the second question, we propose an automatic contentlinking algorithm based on conditional random fields. We demonstrate thatautomatically generated linking can still lead to better learning, although themagnitude of the improvement over the unlinked interface is smaller.

Educational Content Linking for Enhancing Learning Need Remediation in MOOCs