Adaptive Hint Timing in Learning Systems Using Answer Probability

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Solution Overview

Problem

Existing learning assistance systems fail to provide hints at optimal times based on the learner's predicted correct answer probability and question difficulty, leading to suboptimal learning efficiency.

Innovation Solution

An information processing system that derives a predicted correct answer probability and time difference for hint presentation, or derives a question difficulty and time difference for hint presentation, using machine learning models to determine appropriate timing for hint delivery based on the learner's ability and question difficulty.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If hints are provided immediately when a learner requests them, then the learner can get support information quickly, but the learner's independent thinking and problem-solving ability may be reduced

Engineering Contradiction:

Improvehint accessibilityVSAvoidlearning efficiency

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

The system dynamically adjusts hint provision timing based on the learner's predicted correct answer probability and actual performance. Instead of fixed immediate provision, the timing becomes adaptive - delaying hints when the learner shows high capability and providing them sooner when struggle is detected, thus balancing accessibility with learning efficiency

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system uses machine learning models to continuously monitor learner performance and provide feedback on predicted correct answer probabilities. This feedback loop enables the system to adjust hint timing based on real-time assessment of learner state, optimizing when hints are provided to maintain both accessibility and learning effectiveness

Inventive Principle:

Principle #23Feedback

2Productivity

If hints are delayed to maintain learner independence, then problem-solving ability is enhanced, but the learner may experience excessive frustration and loss of motivation

Engineering Contradiction:

Improvelearning efficiencyVSAvoidlearner frustration

Core Design Contradiction:

ProductivityVSObject-affected harmful factors

Solution Approach 1:

The system performs preliminary assessment using machine learning models to predict the correct answer probability before the learner actually attempts the problem. Based on this preliminary prediction, the system proactively prepares appropriate hint timing strategies, preventing excessive frustration before it occurs while maintaining learner independence

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system changes the timing parameter of hint provision based on multiple factors including predicted correct answer probability, problem difficulty level, and learner performance history. This parameter adjustment optimizes the balance between maintaining independence and preventing frustration

Inventive Principle:

Principle #35Parameter changes

3Device complexity

If hints are provided based on fixed timing intervals, then the system is simple to implement, but it cannot adapt to individual learner differences and problem difficulty variations

Engineering Contradiction:

Improvesystem complexityVSAvoidhint timing adaptability

Core Design Contradiction:

Device complexityVSAdaptability or versatility

Solution Approach 1:

The system uses machine learning models to automatically assess learner capability and problem difficulty, enabling self-service adaptation without requiring complex manual configuration. The system serves itself by continuously learning from learner performance data and autonomously optimizing hint timing strategies for different learners and problems

Inventive Principle:

Principle #25Self-service

4Productivity

If the system monitors learner performance closely to optimize hint timing, then learning efficiency is improved, but the system complexity and data processing requirements increase

Engineering Contradiction:

Improvelearning efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system replaces complex manual monitoring and decision-making mechanisms with machine learning models that automatically analyze learner performance data and determine optimal hint timing. This substitution reduces the need for complex rule-based systems while maintaining high learning efficiency through intelligent automation

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentEP4693161A1Information processing device, information processing method, information processing system, and program

Publication Date: 2026.02.11 CASIO COMPUTER CO LTD

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AI summary

An information processing device (10) includes a processor (11) that executes first processing of deriving a predicted correct answer probability being a probability that a learner is predicted to correctly answer a first question, and deriving a time difference between a timing for displaying the first question and a timing for displaying hint information being information as a hint of the first question, based on the predicted correct answer probability, or second processing of acquiring a degree of difficulty of the first question answered by the learner, and deriving a time difference between a timing for displaying the first question and a timing for displaying the hint information, based on the degree of difficulty, and generates data for displaying the first question and the hint information for the learner, based on the time difference.