Adaptive Learning Problem Generation via Changeable Area Detection
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Solution Overview
Problem
Conventional methods for creating learning problems struggle to adapt to specific situations or learners, as they rely on random changes to formulas or texts, failing to reflect the context and resulting in a lack of variety and suitability in learning materials.
Innovation Solution
A method and system that determine changeable areas in reference learning problems, adjust data within specified ranges, and create new problems based on changed data, allowing for adaptive modification of visual elements and difficulty levels to generate diverse and tailored learning materials.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If learning problems are created by randomly determining numbers or characters, then a large number of learning problems can be generated, but it is difficult to reflect the context of a learning situation or a learner, making it difficult to create learning problems suitable for a specific situation or a specific learner
Solution Approach 1:
The patent applies local quality by identifying and modifying specific changeable areas (such as numerical values, problem conditions, or answer options) within the learning problem template, rather than randomly changing entire problems. This allows targeted adaptation to learner needs while maintaining the overall problem structure and context.
Solution Approach 2:
The system changes specific parameters (numerical values, difficulty levels, problem conditions) within defined ranges to generate varied learning problems. This enables systematic adaptation to different learner levels and situations while ensuring problems remain valid and educationally sound.
2Ease of manufacture
If new learning problems are created by making limited changes only to the formulas or texts included in existing learning problems, then the creation process is simple, but it is difficult to create a variety of learning problems
Solution Approach 1:
The patent segments the learning problem into multiple changeable areas (formulas, texts, numerical values, problem conditions, answer options). By allowing independent modification of each segment within its defined range, the system achieves both simplicity in the creation process and high variety in the generated problems.
Solution Approach 2:
The system dynamically adjusts which areas are changed and by how much, based on learner characteristics and learning situations. This dynamic approach enables the same base problem to generate diverse variations suitable for different learners while maintaining a simple automated creation process.
Data Source
AI summary
A method for creating learning problems is provided. The method includes the steps of: with reference to setting information on a reference learning problem, determining changeable areas in the reference learning problem, and determining ranges of data change in the changeable areas; changing data included in at least one of the determined changeable areas, within the range of data change in the at least one changeable area; and creating a new learning problem from the reference learning problem on the basis of the changed data.


