Exploring the Role of User Comments Throughout the Stages of Video-Based Task-Learning

2026-03-12

AI Summary

This study addresses the limited understanding of how user comments function across different stages of task-based video learning, a gap that hinders their potential to compensate for missing tacit knowledge and personalized support in instructional videos. Through semi-structured interviews with 14 users and co-watching experiments involving 8 pairs, complemented by thematic analysis, the research reveals for the first time the critical roles comments play throughout the learning process—specifically in information supplementation, contextual adaptation, and cognitive coordination. Findings indicate a significant misalignment between current comment implementations and learners’ evolving needs across the task-learning trajectory. Building on these insights, the work proposes a design direction that deeply integrates comments into the learning interaction flow, offering novel strategies and practical recommendations to enhance the task-support capabilities of video learning platforms.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Machine Learning: Learning Preferences or Rankings
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Learning tasks through videos is a dynamic way to acquire skills by witnessing entire processes. However, compared to in-person demonstrations, videos may omit tacit knowledge, including subtle details and contextual nuances. Users' unique circumstances, like missing ingredients in a recipe, may also require adaptation beyond the video content. To fill these gaps, many users turn to the comment section, seeking additional guidance and interactions with creators or peers to personalize their experience. Despite their importance, there is limited understanding of how users engage with and apply comments in task-learning scenarios. In our study, we explore the role of comments in video-based task-learning through interviews with 14 users, and co-watching sessions with eight. Our findings show that while comments are critical for learning, they are poorly integrated into all stages of the learning process. Based on our findings, we outline design opportunities to better utilize comments in video-based task-learning.

Problem

• video-based task-learning
• user comments
• tacit knowledge
• learning process
• personalization

Innovation

• video-based learning
• user comments
• task-learning
• co-watching
• design opportunities