  Since its introduction in 2011, there have been over 4000 MOOCs on various
subjects on the Web, serving over 35 million learners. MOOCs have shown the
ability to 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, which adversely affects learning
experience. 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 takes place when learners are surveying course materials, better content
navigation may help learners find supporting information to resolve their
confusion and thus improve learning outcome and experience. To support our
conjecture, we present end-to-end studies to investigate our framework around
two research questions: 1) can manually generated linking improve learning? 2)
can learning content be generated with machine learning methods? For studying
the first question, we built an interface that present learning materials and
visualize the linking among them simultaneously. We found the interface enables
users to search for desired course materials more efficiently, and retain more
concepts more readily. For the second 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.
