The increasing popularity of Massive Open Online Courses (MOOCs) has resulted in huge number of courses available over the Internet. Typically, a learner can type a search query into the look-up window of a MOOC platform and receive a set of course suggestions. But it is difficult for the learner to select lectures out of those suggested courses and learn the desired information efficiently. In this paper, we propose to structure the lectures of the various suggested courses into a map (graph) for each query entered by the learner, indicating the lectures with very similar content and reasonable sequence order of learning. In this way the learner can define his own learning path on the map based on his interests and backgrounds, and learn the desired information from lectures in different courses without too much difficulties in minimum time. We propose a series of approaches for linking lectures of very similar content and predicting the prerequisites for this purpose. Preliminary results show that the proposed approaches have the potential to achieve the above goal.
Bibliographic reference. Shen, Sheng-syun / Lee, Hung-yi / Li, Shang-wen / Zue, Victor / Lee, Lin-shan (2015): "Structuring lectures in massive open online courses (MOOCs) for efficient learning by linking similar sections and predicting prerequisites", In INTERSPEECH-2015, 1363-1367.