Machine Learning Framework for Customized Educational Content
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Solution Overview
Problem
Conventional educational methods struggle to provide individually customized content due to subjective analysis and reliance on manual tagging, leading to inefficient learning and loss of interest among students, as they fail to accurately identify and address individual weak points.
Innovation Solution
A data analysis framework that collects and analyzes user and question data using choice item parameters to generate modeling vectors, calculating selection probabilities and providing user-customized learning content by excluding personal intervention and applying machine-learning techniques to cluster users and questions based on their attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional subjective analysis methods are used to analyze student performance, then the analysis process is simple and quick, but the precision and accuracy of identifying individual weak points deteriorates
Solution Approach 1:
The patent segments the analysis process into distinct components: question difficulty analysis, student ability analysis, and choice item parameter analysis. Each component processes specific aspects of the data independently, then integrates results to provide comprehensive feedback. This segmentation enables precise identification of weak points while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent introduces choice item parameters as additional dimensions for analyzing student responses. By incorporating parameters such as choice item attraction, discrimination ability, and difficulty levels, the system transforms raw response data into multidimensional analytical parameters. This parameter expansion significantly improves measurement precision in identifying student weaknesses without requiring proportional increases in system complexity.
2Adaptability or versatility
If packaged educational content is provided to all students, then the content delivery is efficient and standardized, but the adaptability to individual student needs deteriorates
Solution Approach 1:
The patent implements a dynamic content recommendation system that continuously adapts to individual student performance. The system dynamically generates personalized learning paths by analyzing real-time student responses and adjusting content selection accordingly. This dynamic adaptation enables customized content delivery while maintaining high learning efficiency through data-driven optimization of study sequences and difficulty progression.
Solution Approach 2:
The patent incorporates continuous feedback loops where student performance data is analyzed to generate insights about individual weaknesses, which then inform subsequent content recommendations. The system provides feedback to both students (through personalized recommendations) and educators (through analytical reports), enabling ongoing optimization of learning paths. This feedback mechanism ensures high adaptability while maintaining productivity through evidence-based adjustments.
3Reliability
If manual tagging and subjective experience are used for question analysis, then the implementation cost is low, but the reliability and objectivity of analysis results deteriorates
Solution Approach 1:
The patent implements self-service mechanisms where the system automatically analyzes question characteristics and student responses without requiring manual intervention. The automated analysis framework processes choice item parameters, calculates difficulty levels, and generates reliability metrics independently. This self-service capability significantly improves objectivity and reliability of analysis results while the modular architecture keeps system complexity manageable through automated routine operations.
Data Source
AI summary
Disclosed is a method of providing user-customized learning content. The method includes: a step a of configuring a question database including one or more multiple-choice questions having one or more choice items and collecting choice item selection data of a user for the questions; a step b of calculating a modeling vector for the user based on the choice item data and generating modeling vectors for the questions according to each choice item; and a step c of calculating choice item selection probabilities of the user based on the modeling vectors of the user and the modeling vectors of the questions.