User Learning Ability Evaluation via Search Matrix Analysis
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Solution Overview
Problem
Conventional educational content recommendation technologies fail to provide the best educational content tailored to a user's learning ability, relying solely on search information and webpage reliability without considering the user's knowledge level or learning ability.
Innovation Solution
A method and system that acquire search information, extract relevant question information, calculate learning ability, and select target solution or webpage content based on expected educational effect indices, optimizing content recommendation for maximum educational impact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional technologies provide solutions based on search information and webpage reliability, then solution availability is improved, but educational effectiveness deteriorates because user learning ability is not considered
Solution Approach 1:
The system changes the parameters of content recommendation by introducing learning ability scores and knowledge level assessments. Instead of relying solely on webpage reliability metrics, the system evaluates user-specific parameters (learning ability, knowledge level) and matches them with content parameters (difficulty level, educational value) to optimize educational effectiveness while maintaining solution availability
Solution Approach 2:
The system performs preliminary assessment of user learning ability and knowledge level before recommending educational content. By pre-evaluating user characteristics through search behavior analysis and interaction data, the system prepares personalized content recommendations in advance, ensuring both solution availability and educational effectiveness from the outset
2Reliability
If the system provides high-reliability webpages based on search information, then information accuracy is improved, but adaptability to individual learning needs deteriorates
Solution Approach 1:
The system applies local quality by tailoring content recommendations to individual user characteristics. Instead of providing uniform high-reliability content to all users, the system adjusts content selection based on each user's learning ability and knowledge level, creating locally optimized recommendations that maintain information accuracy while adapting to individual needs
Solution Approach 2:
The system introduces dynamics by continuously updating content recommendations based on changing user states. As users interact with content and their learning ability evolves, the system dynamically adjusts the difficulty level and type of content provided, maintaining both information accuracy and adaptability to current learning needs
3Device complexity
If the system recommends educational content without considering learning ability, then system complexity is reduced, but educational effect deteriorates
Solution Approach 1:
The system implements self-service by automatically assessing user learning ability and autonomously selecting appropriate content. Through automated analysis of search behavior, interaction patterns, and performance data, the system performs what would otherwise require manual assessment and curation, maintaining low complexity while achieving high educational effect through intelligent automation
Data Source
AI summary
According to an embodiment of a recommending educational content method includes: acquiring search information of target user; acquiring learning set information based on the search information; acquiring a search database of a plurality of users based on the leaning set information, the search database including user identification information and a reference value allocated according to whether the user searches for a question included in the learning set information; allocating a feature value according to whether to search for at least one question included in the learning set information based on the search information; generating a first matrix based on the reference value of the search database and the feature value related to the target user; transforming the first matrix into a second matrix based on similarity of the reference value and the feature value; and calculating a learning ability score of the target user based on the second matrix.