An adaptive time reduction technique for video lectures

Lecture videos are a widely used resource for learning. A simple way to create videos is to record live lectures, but these videos end up being lengthy, include long pauses and repetitive words making the viewing experience time consuming. While pauses are useful in live learning environments where students take notes, we question the value of pauses in video lectures. Techniques and algorithms that can shorten such videos can have a huge impact in saving students' time and reducing storage space. We study this problem of shortening videos by removing long pauses and adaptively modifying the playback rate by emphasizing the most important sections ofthe video and its effect on the student community. The playback rate is designed in such a way to play uneventful sections faster and significant sections slower. Important and unimportant sections of a video are identified using textual analysis. We use an existing speech-to-text algorithm to extract the transcript and apply latent semantic analysis and standard information retrieval techniques to identify the relevant segments of the video. We compute relevance scores of different segments and propose a variable playback rate for each of these segments. The aim is to reduce the amount of time students spend on passive learning while watching videos without harming their ability to follow the lecture. We validate our approach by conducting a user study among computer science students and measuring their engagement. The results indicate no significant difference in their engagement when our method is compared to the original unedited video.