Personalized Learning Information Provision System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional learning methods provide uniform learning information in a fixed order, failing to account for individual learner weaknesses and characteristics, leading to inefficient learning as all information is provided regardless of necessity.
Innovation Solution
A system that determines a personalized frequency index of learning units based on supplementary learning patterns, using a knowledge map to identify units with a predefined correlation, allowing for personalized supplementary learning information to be provided only to the learner, optimizing learning efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If learning information is provided sequentially according to a predetermined learning system, then the learning structure is systematic and complete, but all learners receive uniform information regardless of their individual weaknesses or characteristics
Solution Approach 1:
The system collects supplementary learning patterns from learners and uses this feedback to dynamically determine personalized frequency indexes. The knowledge map is updated based on learner performance data, creating a closed-loop system that continuously adapts learning recommendations to individual needs while maintaining systematic structure.
Solution Approach 2:
The knowledge map is pre-constructed with all learning units and their correlations established before learners begin. This preliminary structuring allows the system to quickly query and determine personalized learning units without complex real-time calculations, reducing computational complexity while enabling personalization.
2Productivity
If all learning information is provided to learners, then comprehensive coverage is achieved, but learning efficiency decreases due to provision of unnecessary information
Solution Approach 1:
The system applies different information provision strategies to different learners based on their specific weaknesses. Each learner receives a customized subset of learning units determined by their personalized frequency indexes, ensuring that necessary information is provided while unnecessary information is omitted, thereby improving learning efficiency without compromising comprehensive coverage of needed topics.
3Adaptability or versatility
If a teacher provides tutoring to multiple learners, then personalized attention can be given, but the time and effort required increases significantly
Solution Approach 1:
The system enables learners to receive personalized tutoring recommendations automatically without requiring direct teacher intervention for each learner. The automated determination of personalized supplementary learning units based on knowledge maps and supplementary learning patterns allows learners to access customized learning paths independently, significantly reducing the time teachers need to spend on individualized instruction while maintaining personalized attention quality.
Data Source
AI summary
A method for providing learning information is provided. The method includes: with reference to two or more supplementary learning patterns experienced by a learner, determining an personalized frequency index related to at least one learning unit commonly included in the two or more supplementary learning patterns; and determining a learning unit having a personalized frequency index higher than or equal to a pre-configured reference, as a personalized supplementary learning unit to be provided to the learner. Each of the two or more supplementary learning patterns includes two or more learning units having a predetermined correlation, and the predetermined correlation is acquired from a knowledge map, which includes two or more nodes corresponding to the two or more learning units and at least one link which connects the two or more nodes and is assigned the correlation between the two or more learning units.