Analyzing Student Viewing Patterns in Lecture Videos

A large amount of educational content is available as lecture videos, which record teachers as they proceed through a course. Students watch these videos in different ways. They rewind, skip forward, watch some scenes repeatedly. This work investigates what can be learned by analyzing such viewing patterns. We show how to use machine learning techniques to analyze such data, and present the outcomes of an analysis of data collected from the interactions of 2992 students in 253 courses. The viewing pattern were put into relation to seven different variables, such as the final score of the student and the rating teachers received from students Our analysis shows that some variables, such as the teacher rating, were indeed predictable from the viewing patterns.