Knowledge Point Mark Generation for Video Learning
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Solution Overview
Problem
Current video learning systems require learners to browse entire video files to determine if they meet learning needs and to locate specific key points, causing inefficiency due to limited title-based searching and the need to manually navigate through playback timelines.
Innovation Solution
A knowledge point mark generation system that captures and analyzes computer screen and blackboard images, converts sound signals to text, and identifies keywords and identities to statistically analyze and set marks on a timeline, allowing learners to easily locate key points without browsing the entire video.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If learners search for video files by title only, then the search process is simple, but the learner cannot efficiently determine whether the video file meets learning needs and must browse the entire video file
Solution Approach 1:
The system performs preliminary extraction of knowledge points and generation of time-stamped marks during video processing, before the learner views the video. This allows learners to quickly locate key content without browsing the entire video, as the knowledge point marks are already prepared and embedded in the video metadata.
Solution Approach 2:
The patent introduces knowledge point marks as an intermediary element between the video content and the learner. These marks serve as navigation indicators that enable learners to efficiently locate specific knowledge points without having to browse the entire video or rely solely on title information.
2Device complexity
If learners manually navigate through playback timeline to find key points, then no additional tools are needed, but the review process becomes inconvenient and time-consuming
Solution Approach 1:
The system pre-identifies and marks knowledge points during video processing, so that when learners want to review, the key points are already located and marked on the playback timeline. This eliminates the need for learners to manually drag the progress pointer or fast-forward to find key points, significantly improving review convenience.
3Productivity
If the system extracts keywords from video content, then learning efficiency improves, but the system complexity increases due to multiple devices and processing steps
Solution Approach 1:
The patent divides the video processing system into distinct functional modules: a capturing device for obtaining video and audio, a speech recognition device for converting audio to text and identifying speakers, and a processing device for extracting keywords and generating knowledge point marks. This segmentation allows each component to perform its specific function independently, making the overall system more manageable and implementable despite the increased complexity.
Data Source
AI summary
A knowledge point mark generation system and a method thereof are provided. The system and the method thereof can obtain a label vocabulary by performing the analysis procedure on at least one second candidate vocabulary repeated by sound in a class, at least one first candidate vocabulary repeated by text during class, at least one second keyword highlighted by sound during class, and at least one first keyword highlighted by text during class according to their weights, and then set knowledge point marks on a timeline of a video file taken during class according to time periods when the label vocabulary appears. Thus, a learner can know the knowledge points of the class and their video clips in the video file without browsing the entire video file, so that it is convenient for the learner to learn or review the key points of the class.