Usage-based Summaries of Learning Videos

TL;DR

This paper introduces a system that analyzes student playback data of educational videos to generate usage-based summaries, helping students identify important segments for revision.

Contribution

The paper presents a novel method for scoring and visualizing video segments based on collective student playback behavior, aiding in efficient revision.

Findings

Effective identification of popular video segments

Enhanced revision efficiency for students

GDPR-compliant anonymized data collection

Abstract

Much of the delivery of University education is now by synchronous or asynchronous video. For students, one of the challenges is managing the sheer volume of such video material as video presentations of taught material are difficult to abbreviate and summarise because they do not have highlights which stand out. Apart from video bookmarks there are no tools available to determine which parts of video content should be replayed at revision time or just before examinations. We have developed and deployed a digital library for managing video learning material which has many dozens of hours of short-form video content from a range of taught courses for hundreds of students at undergraduate level. Through a web browser we allow students to access and play these videos and we log their anonymised playback usage. From these logs we score to each segment of each video based on the amount of playback it receives from across all students, whether the segment has been re-wound and re-played in the same student session, whether the on-screen window is the window in focus on the student's desktop/laptop, and speed of playback. We also incorporate negative scoring if a video segment is skipped or fast-forward, and overarching all this we include a decay function based on recency of playback, so the most recent days of playback contribute more to the video segment scores. For each video in the library we present a usage-based graph which allows students to see which parts of each video attract the most playback from their peers, which helps them select material at revision time. Usage of the system is fully anonymised and GDPR-compliant.