Topic Progression Model for Content Management Systems
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Solution Overview
Problem
Conventional techniques are not well-suited for extracting and organizing topics in educational systems with diverse content types, leading to high resource investments and scalability issues, and lack a unified relationship between topics from different content types.
Innovation Solution
A content management system generates topic progressions by extracting and pairing topics based on their proximity in documents, calculating complexity measures, and recommending progressively more challenging topics to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate systems are used to extract topics from each content type, then topic extraction can be performed for each content type, but resource investment increases and scalability decreases
Solution Approach 1:
The patent combines multiple content type-specific topic extraction systems into a single unified topic extraction system that can handle diverse content types (textbooks, articles, videos, images, etc.). This unified system uses a common machine learning model trained on multi-type educational content, eliminating the need for separate extraction systems for each content type while maintaining extraction accuracy across all content types.
2Reliability
If separate systems are used to extract topics from each content type, then topic extraction can be performed for each content type, but scalability is reduced
Solution Approach 1:
The patent creates a universal topic extraction system that can process multiple content types through a single interface. The system uses a multi-type machine learning model that has been trained to extract topics from various educational content types, allowing the same system to scale across different content types without requiring separate specialized systems for each type.
3Ease of manufacture
If conventional techniques are used for topic extraction, then existing methods can be applied, but a unified relationship between topics from different content types cannot be established
Solution Approach 1:
The patent transforms the topic extraction process by changing the parameters of the machine learning model to accommodate multiple content types. The system uses a multi-type model with adjusted parameters that can process text, video, audio, and other educational content formats, enabling the establishment of unified topic relationships across different content types while maintaining ease of implementation through automated processing.
4Device complexity
If topics are extracted without complexity measures, then topic extraction is simpler, but progressive learning pathways cannot be generated
Solution Approach 1:
The patent performs preliminary analysis by calculating complexity measures for extracted topics before organizing them into learning pathways. The system computes complexity metrics (such as reading level, conceptual difficulty, or prerequisite relationships) for each topic, then uses these pre-calculated measures to automatically generate progressive learning sequences that adapt to user needs, simplifying the overall process of creating personalized learning paths.
Data Source
AI summary
A content management system receives a plurality of topics extracted from documents stored by the system. Pairings between the topics are generated, where a pairing between two topics is generated responsive to the two topics appearing in proximity to one another in one or more of the documents. A complexity of each received topic is also determined. The content management system generates a progression of the topics based on the complexity of the topics and the pairings between the topics. The progression comprises a sequential ordering of paired topics, in which a topic in the ordering has a higher complexity than a preceding topic. Responsive to a user of the content management system accessing content associated with a topic in the progression, a next topic in the progression is recommended to the user.