CoKnowledge: Supporting Assimilation of Time-synced Collective Knowledge in Online Science Videos

TL;DR

CoKnowledge is a tool designed to improve understanding of science videos by filtering and organizing time-synced comments, significantly enhancing viewers' comprehension and recall of collective knowledge.

Contribution

This paper introduces CoKnowledge, a novel processing pipeline and tool that filters, classifies, and clusters danmaku comments to support knowledge assimilation in science videos.

Findings

CoKnowledge significantly improved comprehension and recall.

Participants found the organized comments more helpful.

The study provided design insights for future support tools.

Abstract

Danmaku, a system of scene-aligned, time-synced, floating comments, can augment video content to create 'collective knowledge'. However, its chaotic nature often hinders viewers from effectively assimilating the collective knowledge, especially in knowledge-intensive science videos. With a formative study, we examined viewers' practices for processing collective knowledge and the specific barriers they encountered. Building on these insights, we designed a processing pipeline to filter, classify, and cluster danmaku, leading to the development of CoKnowledge - a tool incorporating a video abstract, knowledge graphs, and supplementary danmaku features to support viewers' assimilation of collective knowledge in science videos. A within-subject study (N=24) showed that CoKnowledge significantly enhanced participants' comprehension and recall of collective knowledge compared to a baseline with unprocessed live comments. Based on our analysis of user interaction patterns and feedback on design features, we presented design considerations for developing similar support tools.