Educational Content Recommendation System Using Learning Ability Assessment

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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 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, using a device with a transceiver and controller to communicate with user terminals and databases to provide optimized educational content.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If conventional technologies provide solutions based only on search information and webpage reliability, then the system is simple to operate, but the educational content is not optimized for user learning ability

Engineering Contradiction:

Improvelearning ability assessment accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system performs preliminary analysis of user search information to calculate learning ability information before recommending educational content. By pre-processing search data and establishing learning ability metrics in advance, the system can make accurate recommendations without adding complex real-time processing requirements.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent introduces an intermediary learning ability information calculation module that bridges search information and educational content recommendation. This intermediary component processes search data to derive learning ability metrics, which then guide content selection, simplifying the overall system architecture while improving recommendation accuracy.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If the system calculates learning ability information and selects content based on expected educational effect index, then the educational effect is maximized, but the calculation and selection process becomes more complex

Engineering Contradiction:

Improveeducational effect efficiencyVSAvoidcontent selection complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system transforms the content selection problem into a parameter optimization problem by calculating an expected educational effect index. This index serves as a quantitative parameter that combines learning ability information with content characteristics, enabling systematic selection of optimal educational content through parameter comparison rather than complex qualitative analysis.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent replaces manual or heuristic content selection methods with an automated indexing system. By substituting the mechanical process of evaluating educational content with an computational index calculation based on learning ability and content parameters, the system achieves higher efficiency with manageable complexity.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Reliability

If the system provides personalized educational content based on learning ability, then the educational content quality improves, but the data processing requirements increase

Engineering Contradiction:

Improvecontent recommendation reliabilityVSAvoidsearch information processing load

Core Design Contradiction:

ReliabilityVSLoss of information

Solution Approach 1:

The system extracts only the essential features from search information that are relevant to learning ability assessment. By selectively extracting key parameters from raw search data rather than processing all available information, the system reduces data processing load while maintaining recommendation reliability.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent segments the data processing into distinct stages: search information collection, learning ability information calculation, and content recommendation. This segmentation allows each stage to process only necessary data with appropriate complexity, reducing overall information processing requirements while improving recommendation quality.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS20230005382A1Method, device, and system for recommending solution content maximizing an educational effect for users

Publication Date: 2023.01.05 RIIID CO

AI summary

According to an embodiment of the present invention, a method of recommending educational content includes acquiring search information of a user, extracting searched question information based on the search information, acquiring a solution content set related to the question information, the solution content set including first solution information and second solution information, calculating learning ability information of the user based on the search information, calculating an index related to an expected educational effect based on the learning ability information and the solution content set, selecting target solution content from the solution content set based on the index, and transmitting the target solution content.