Encoder-Decoder Learning Path Sequencing for Personalized Mastery
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Solution Overview
Problem
Existing learning path recommendation methods struggle to ensure personalization and effectiveness due to reliance on paths used by other users, limiting the customization and learning impact for individual users.
Innovation Solution
A data processing method that determines a learning order of knowledge points based on current user states and feature representations, using an encoder and decoder to identify relationships and optimize the sequence generation, incorporating attention mechanisms and LSTM networks to enhance personalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If collaborative filtering or content-based filtering is used to recommend learning paths based on other users' historical data, then the recommendation speed is improved, but the personalization degree and learning effect deteriorate
Solution Approach 1:
The patent segments the learning path recommendation into two parts: using historical user data for fast initial recommendation and using real-time student state (mastery level, attention) for dynamic personalization adjustments. This allows the system to maintain speed while improving personalization.
Solution Approach 2:
The patent introduces dynamic elements by continuously tracking student state (mastery level, attention level) and adjusting the learning path in real-time based on current performance, rather than relying solely on static historical data from other users.
2Productivity
If complete learning paths are recommended at the beginning based on user clustering, then the recommendation efficiency is improved, but the adaptability to individual learning states deteriorates
Solution Approach 1:
The patent transforms the static complete learning path into a dynamic sequence that adapts as the student progresses. The system continuously updates the recommended next knowledge point based on real-time mastery level and attention level, allowing the path to flexibly adjust while maintaining overall efficiency.
Solution Approach 2:
The patent performs preliminary clustering and path generation based on historical data to establish an initial efficient learning path, then uses real-time state monitoring to make targeted adjustments, combining the benefits of both pre-computation and real-time adaptation.
3Device complexity
If paths are selected from historical user data, then the system complexity is reduced, but the measurement precision of individual learning needs deteriorates
Solution Approach 1:
The patent introduces an intermediary layer (the reinforcement learning agent) that translates simple historical path data into precise individualized recommendations by incorporating real-time student state measurements, thereby improving measurement precision without proportionally increasing system complexity.
Solution Approach 2:
The patent implements continuous feedback loops where student performance on knowledge points feeds back into the recommendation system, allowing the system to progressively refine its understanding of individual learning needs based on actual performance data rather than relying solely on complex pre-analysis.
Data Source
AI summary
A data processing method includes obtaining feature representations of a plurality of knowledge points and a first learning state of a user, the first learning state indicates a user's level of mastery of a learned knowledge point, and the plurality of knowledge points are different from the learned knowledge point; and obtaining, by using a decoder based on the feature representations of the plurality of knowledge points, the first learning state, and a learning objective, a learning order corresponding to the plurality of knowledge points, the decoder is configured to identify a relationship between the feature representations of the plurality of knowledge points and the learning objective in the first learning state. The learning order of the plurality of knowledge points is determined in a sequence generation manner based on a current learning state of the user.