Learning Content Recommendation System Using Expected Score
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Solution Overview
Problem
Current learning content recommendation systems, such as those using collaborative filtering, often recommend questions with a high probability of being answered incorrectly, which may not align with the user's skill level, leading to inefficient learning and skill development.
Innovation Solution
A learning content recommendation apparatus and system that calculates an expected score based on user performance and learning degree, recommending questions that offer the highest score improvement by reflecting the user's learning effect, using components like an estimated score calculator, correct answer rate predictor, and recommended question determiner, which incorporate artificial neural networks for predicting answer rates and reflecting learning degrees.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If collaborative filtering recommends questions with the highest probability of being answered incorrectly, then the system can identify challenging questions for users, but the recommended questions may not align with user skill level, reducing learning efficiency
Solution Approach 1:
The patent changes the recommendation parameter from simple correct answer rate to expected score improvement, which incorporates multiple factors including user ability, question difficulty, and learning degree. This parameter transformation resolves the contradiction by enabling the system to simultaneously identify challenging questions and align them with user skill levels.
Solution Approach 2:
The patent introduces feedback mechanisms by calculating learning degree based on user performance history and incorporating it into the expected score calculation. This feedback loop allows the system to adapt recommendations to individual user progress, resolving the contradiction between challenge and alignment.
2Adaptability or versatility
If the system recommends high-level questions to users with lower scores, then users can access challenging content, but learning efficiency decreases due to mismatched difficulty
Solution Approach 1:
The patent transforms the recommendation criterion from static correct answer rate to dynamic expected score improvement, which adapts to user ability. This allows the system to recommend appropriately challenging questions that match user skill levels, resolving the contradiction between access to challenging content and learning efficiency.
Solution Approach 2:
The patent makes the recommendation system dynamic by continuously updating user ability estimates and learning degrees based on performance feedback. This dynamic adaptation enables the system to provide challenging content at appropriate difficulty levels, resolving the contradiction between challenge accessibility and learning efficiency.
3Device complexity
If the system uses simple correct answer rate prediction, then the recommendation process is straightforward, but it fails to reflect user learning progress and skill improvement
Solution Approach 1:
The patent merges multiple information sources including user ability, question difficulty, correct answer rate, and learning degree into a unified expected score metric. This integration resolves the contradiction by maintaining relative simplicity while incorporating comprehensive learning effect information.
Solution Approach 2:
The patent creates a multi-functional recommendation metric that simultaneously captures challenge level, skill alignment, and learning progress. This universal expected score calculation resolves the contradiction by embedding multiple information dimensions without proportionally increasing system complexity.
Data Source
AI summary
A learning content recommendation apparatus system, or method may be provided for determining a recommended question by reflecting a learning effect of a user. The apparatus, system or method may include: predicted score calculator configured to, on the basis of user information including a question previously solved by a user and a response of the user to the question, calculate predicted score information including a maximum predicted score and a minimum predicted score; a correct answer rate predictor configured to predict correct answer rate information, which is a probability that the user correctly answers the a candidate question, on the basis of the user information; and a recommended question determiner configured to calculate an expected score on the basis of one or more of the predicted score information, the correct answer rate information, and a degree of learning, and configured to determine a recommended question according to the expected score.