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, I 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.

I study this problem of shortening videos by removing long pauses and adaptively

modifying the playback rate by emphasizing the most important sections of the 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. I 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. I 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. I validate the approach by conducting a user study among

computer science students and measuring their engagement. The results indicate

no significant difference in their engagement when this method is compared to the

original unedited video.