Adaptive Learning System Using Multilevel Knowledge Organization
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Solution Overview
Problem
Existing Internet-based education systems fail to provide individualized learning, lack user-to-user interaction, and do not leverage the dynamic nature of modern computer systems or collective intelligence, resulting in inefficient and discouraging learning experiences due to rigid content and inflexible interfaces.
Innovation Solution
A computer-based learning system that organizes knowledge into a predefined multilayer arrangement, using interactive interfaces to provide personalized learning paths based on user feedback, allowing users to suggest content and track their learning history, and adaptively determine the next knowledge points to study.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If fixed knowledge content and rigid teaching materials are used, then system complexity is reduced and ease of operation is improved, but adaptability to individual user needs deteriorates and learning efficiency decreases
Solution Approach 1:
The patent implements dynamic adaptability by enabling the system to automatically adjust teaching materials, knowledge points, and learning paths based on real-time analysis of user feedback, learning history, and performance data. The system transitions from static fixed content to dynamic adaptive content that evolves with each user interaction, resolving the contradiction between operational simplicity and individualized adaptability.
Solution Approach 2:
The system changes multiple parameters simultaneously including knowledge point selection, content difficulty level, presentation format, and learning pace based on analyzed user characteristics. By dynamically adjusting these parameters according to user feedback and performance, the system achieves high adaptability while maintaining ease of operation through automated parameter optimization.
2Adaptability or versatility
If individualized learning paths are implemented, then adaptability and learning efficiency are improved, but system complexity and device complexity increase
Solution Approach 1:
The patent implements self-service automation where the system autonomously analyzes user feedback, evaluates learning progress, selects appropriate knowledge points, and generates personalized learning paths without requiring manual intervention. This automated self-service approach handles the complexity internally while presenting a simple interface to users, resolving the contradiction between individualized adaptability and system complexity.
Solution Approach 2:
The system continuously collects and analyzes user feedback from interactions with teaching materials, exercises, and assessments. This feedback loop enables automatic adjustment of learning paths and content selection, allowing the system to manage complexity through data-driven automation rather than requiring complex manual configuration for each user.
3Measurement precision
If comprehensive learning history recording and analysis are implemented, then adaptability and measurement precision are improved, but loss of time for data processing increases
Solution Approach 1:
The patent implements preliminary action by pre-establishing the framework for learning history recording and analysis before actual learning occurs. The system is pre-configured to automatically capture, store, and初步 analyze user interactions, eliminating the need for time-consuming post-processing and enabling immediate use of learning data for adaptive content delivery.
Solution Approach 2:
The system maintains continuous automated recording and analysis of learning history without interrupting the user's learning flow. By performing data collection and initial analysis continuously in the background, the system achieves high measurement precision while minimizing time loss, as the analysis occurs seamlessly during normal system operation rather than requiring separate processing time.
4Ease of manufacture
If static knowledge content is used, then manufacturing precision and ease of manufacture are improved, but adaptability and user engagement deteriorate
Solution Approach 1:
The patent transforms static knowledge content into dynamic adaptive content that automatically adjusts based on user analysis. The system maintains the ease of manufacturing static content while adding a layer of dynamic adaptation through automated content selection and customization based on user feedback and learning history, resolving the contradiction between ease of manufacture and adaptability.
Data Source
AI summary
A computer-based learning system uses knowledge points organized with a predefined multilevel arrangement. Each knowledge point has an information set which may include a knowledge content, an evaluation content and a solution content. For a given knowledge point, the learning system provides the knowledge content and evaluation content, analyzes the user's answers to the evaluation content and determines the next knowledge point to be studied by the user based on the user's answers and the predefined multilevel arrangement of the knowledge points. The learning system thus provides a different learning course for different users to achieve individualized learning. User's learning history may be recorded to facilitate reviews by the user and improve the selection of the next knowledge point. Users may provide feedbacks on knowledge contents and evaluation contents, and may even suggest their own knowledge contents and evaluation contents to improve the learning system and user participation.