Question Hint Timing Control Based on Learner 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 or 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 degree of difficulty and time difference for hint presentation, to generate data for displaying questions and hints at appropriate times, using machine learning models and IRT to personalize hint delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If hint information is provided immediately when a learner requests it, then the learner can get support quickly, but the learner's independent thinking and problem-solving ability may be reduced
Solution Approach 1:
The system dynamically adjusts the timing of hint provision based on the learner's predicted correct answer probability and the time elapsed since the question was presented. Instead of providing hints immediately or at fixed intervals, the system calculates optimal timing by considering the learner's current ability state and question difficulty, making the hint provision process adaptive and dynamic rather than static
Solution Approach 2:
The system uses machine learning models to continuously predict the learner's correct answer probability based on their answer history and performance data. This feedback mechanism allows the system to understand the learner's current state and adjust hint timing accordingly, creating a closed-loop control system that optimizes learning outcomes
2Device complexity
If hints are provided based on fixed timing intervals, then the system is simple to implement, but it cannot adapt to individual learner abilities and question difficulties
Solution Approach 1:
The system changes the timing parameter for hint provision based on multiple variables including the learner's predicted correct answer probability, the time elapsed since the question was presented, and the question's difficulty level. By dynamically adjusting this critical parameter rather than using a fixed value, the system achieves personalization while maintaining a relatively simple implementation approach
Solution Approach 2:
The system performs preliminary calculations of the predicted correct answer probability and determines the optimal hint timing in advance, before the learner actually requests the hint. This preliminary action allows the system to prepare personalized timing strategies based on the learner's current state, enabling adaptive hint provision without complex real-time decision-making
Data Source
AI summary
An information processing device includes a processor 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.