2 research outputs found
Semantic topic discovery for lecture video
© Springer Nature Switzerland AG 2020. With more and more lecture, videos are available on the Internet, on-line learning and e-learning are getting increasing concerns because of many advantages such as high degree of interactivity. The semantic content discovery for lecture video is a key problem. In this paper, we propose a Multi-modal LDA model, which discovers the semantic topics of lecture videos by considering audio and visual information. Specifically, the speaking content and the information of presentation slides are extracted from the lecture videos. With the proposed inference and learning algorithm, the semantic topics of the video can be discovered. The experimental results show that the proposed method can effectively discover the meaningful semantic characters of the lecture videos
A Closer Look into Recent Video-based Learning Research: A Comprehensive Review of Video Characteristics, Tools, Technologies, and Learning Effectiveness
People increasingly use videos on the Web as a source for learning. To
support this way of learning, researchers and developers are continuously
developing tools, proposing guidelines, analyzing data, and conducting
experiments. However, it is still not clear what characteristics a video should
have to be an effective learning medium. In this paper, we present a
comprehensive review of 257 articles on video-based learning for the period
from 2016 to 2021. One of the aims of the review is to identify the video
characteristics that have been explored by previous work. Based on our
analysis, we suggest a taxonomy which organizes the video characteristics and
contextual aspects into eight categories: (1) audio features, (2) visual
features, (3) textual features, (4) instructor behavior, (5) learners
activities, (6) interactive features (quizzes, etc.), (7) production style, and
(8) instructional design. Also, we identify four representative research
directions: (1) proposals of tools to support video-based learning, (2) studies
with controlled experiments, (3) data analysis studies, and (4) proposals of
design guidelines for learning videos. We find that the most explored
characteristics are textual features followed by visual features, learner
activities, and interactive features. Text of transcripts, video frames, and
images (figures and illustrations) are most frequently used by tools that
support learning through videos. The learner activity is heavily explored
through log files in data analysis studies, and interactive features have been
frequently scrutinized in controlled experiments. We complement our review by
contrasting research findings that investigate the impact of video
characteristics on the learning effectiveness, report on tasks and technologies
used to develop tools that support learning, and summarize trends of design
guidelines to produce learning video