ON-DEMAND VIDEO TAGGING, ANNOTATION, AND SEGMENTATION IN LECTURE RECORDINGS TO ENHANCE E-LEARNING EFFECTIVENESS
Abstract :
The COVID-19 pandemic has forced much of the academic world to transition into online operations and online learning. Interactions between the teachers and students are carried out via online video conferencing software where possible. All video conferencing software available today is designed for general usage and not for classroom teaching and learning. In this study, we analyzed the features and effectiveness of more than a dozen major video conferencing software that are being used to replace the physical face-to-face learning experiences. While some of the video conferencing software has pause feature but none allow annotation and segmentation of the recording. We propose tagging and annotation during the live streaming to improve direct access to any portion of the recorded video. We also propose automatic segmentation of the video based on the tagging so that the video is short, targeted, and can easily be identified.
Keyword :
Live and On-Demand Video, Video Tagging, Video Annotation, Video Segmentation, ELearning, Remote Learning, Synchronous Lectures, User Interface Design, Human-Computer Interaction.
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