Adaptive Neural Network Resource Allocation for Educational Content
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Solution Overview
Problem
Existing educational content recommendation technologies prioritize advanced algorithms and resources, leading to unequal educational outcomes where users who pay more tend to benefit more, lacking fairness in education.
Innovation Solution
A method and device that acquire learning data, including first and second learning ability information and question answering data, to determine a neural network model based on a user's target learning ability, distributing resources accordingly to recommend personalized educational content, ensuring fairness by adjusting resource allocation based on learning probability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If more advanced algorithms and computing resources are used to improve educational content recommendation, then educational effects are improved, but education fairness deteriorates because users who pay more benefit more
Solution Approach 1:
The patent applies local quality by allocating different computing resources to different users based on their individual learning ability diagnoses. Instead of uniform resource distribution, the system dynamically adjusts neural network model complexity and computing power allocation according to each user's specific learning characteristics, thereby improving educational effects for each user while maintaining fairness through personalized adaptation.
Solution Approach 2:
The system changes parameters dynamically by adjusting the complexity level of neural network models based on diagnosed learning ability. When a user shows difficulty in learning, the system increases computing resources and model complexity; when learning progresses well, it reduces resources. This parameter adjustment resolves the contradiction by making resource allocation adaptive to actual educational needs rather than fixed.
2Measurement precision
If computing resources are increased to enhance recommendation accuracy, then educational effects improve, but resource consumption increases
Solution Approach 1:
The patent implements dynamics by making computing resource allocation flexible and adaptive rather than static. The system continuously monitors user learning progress and adjusts neural network model complexity in real-time, increasing resources only when learning difficulties are detected and reducing them when progress is smooth. This dynamic adjustment maintains high recommendation accuracy when needed while minimizing resource consumption during normal learning phases.
Solution Approach 2:
The system applies partial action by allocating computing resources selectively rather than continuously. Instead of maintaining maximum neural network model complexity for all users at all times, the system provides enhanced computing power only partially - specifically when diagnosis indicates learning difficulties require more sophisticated recommendation strategies. This resolves the contradiction by matching resource intensity to actual educational needs.
Data Source
AI summary
Provided are a device and method for recommending educational content. The method includes acquiring a user's learning data, wherein the learning data includes at least one of the user's first learning ability information at a first time point, the user's second learning ability information at a second time point, and the user's question answering information, acquiring the user's target learning ability information on the basis of the learning data, determining a neural network model on the basis of the target learning ability information, distributing resources corresponding to the determined neural network model, and acquiring educational content to be recommended to the user through the determined neural network model.