论文标题
AI在有效的视觉学习环境中注释的建议，重点是YouTube（AI-EVL）
AI Annotated Recommendations in an Efficient Visual Learning Environment with Emphasis on YouTube (AI-EVL)
论文作者
论文摘要
在本文中，我们创建了一个称为AI-EVL的系统。这是一个基于注释的学习系统。我们将AI扩展到学习经验。如果从主YouTube页面上的用户浏览YouTube视频，而来自AI-EVL系统的用户也可以这样做，则使用的流量将少得多。这是由于忽略了不需要的内容，这也表明带宽使用情况也减少了。该系统旨在嵌入在线学习工具和平台以丰富其课程。在使用Google 2020趋势数据评估系统时，我们能够为每个数据提取丰富的本体论信息。在收集的数据中，有34.86％属于Wolfram，DBPEDIA属于30.41％，Wikipedia属于34.73％。随着视频的播放，随着时间的推移，视频字幕信息在交互和功能上以功能性显示。由于独特的功能，这种有效的视觉学习系统可防止用户的注意力，并使学习更加集中。有关字幕文本的信息以多层显示，包括AI宣传的主题，Wikipedia/dbpedia和Wolfram通过交互式和视觉小部件丰富的文本。
In this article, we create a system called AI-EVL. This is an annotated-based learning system. We extend AI to learning experience. If a user from the main YouTube page browses YouTube videos and a user from the AI-EVL system does the same, the amount of traffic used will be much less. It is due to ignoring unwanted contents which indicates a reduction in bandwidth usage too. This system is designed to be embedded with online learning tools and platforms to enrich their curriculum. In evaluating the system using Google 2020 trend data, we were able to extract rich ontological information for each data. Of the data collected, 34.86% belong to wolfram, 30.41% to DBpedia, and 34.73% to Wikipedia. The video subtitle information is displayed interactively and functionally to the user over time as the video is played. This effective visual learning system, due to the unique features, prevents the user's distraction and makes learning more focused. The information about the subtitle text is displayed in multiple layers including AI-annotated topics, Wikipedia/DBpedia, and Wolfram enriched texts via interactive and visual widgets.
