Fuzzy clustering of lecture videos based on topic modeling

Abstract

Lecture videos constitute an important part of the e-learning paradigm. These online video-lectures contain multimedia materials aimed at explaining complex concepts in a more effective way. The videos are mostly grouped by their subjects. However, often there are overlaps between the subjects, e.g. Mathematics and Statistics. Hence, educational content-wise, some of the lecture videos can belong to more than one subject. When they are labeled by only one subject, students searching for the content of the lecture might miss some of these videos. To solve this problem, we aim to provide a clustering of these lecture videos based on their educational content rather than their titles so that such lectures will not be missed out based on the subject labels. Our novel algorithm uses topic modeling on video transcripts generated by automatic captions to extract the contents of these videos. We choose representative text documents for each of the clusters from the Wikipedia. Then we calculate a similarity between the topics extracted from the videos and those of the representative documents of the clusters. Finally we apply fuzzy clustering based on these similarity values and provide a lecture-content based clustering for these lecture videos. The initial results are plausible and confirm the effectiveness of the proposed scheme.