Adaptive Learning System Using Non-Linear Filters
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Solution Overview
Problem
Current electronic learning systems fail to effectively utilize user behavior data to tailor educational content and assessments, leading to inefficient student progression and lack of personalized learning experiences, as they primarily focus on examination responses without considering user proficiency and behavior, and struggle with processing the vast amount of data generated during educational sessions.
Innovation Solution
A method for updating conditional estimates of user and learning tool characteristics within a computerized learning system using non-linear filters, which generates personalized learning tools and reports by analyzing user interactions, and allows for the storage and retrieval of learning objectives with probabilistic distributions, enabling the creation of customized learning experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional examination-based assessment systems are used, then examination responses can be tracked, but user behavior data and proficiency information are not utilized
Solution Approach 1:
The patent segments the assessment system into multiple components: examination response tracking, user behavior monitoring, proficiency estimation, and learning tool generation. Each component processes specific types of data independently before integrating results, allowing comprehensive data utilization without overwhelming system complexity
Solution Approach 2:
The patent introduces an intermediary processing layer that transforms raw user behavior data and examination responses into meaningful proficiency estimates. This intermediary layer uses statistical models and algorithms to convert complex behavioral patterns into actionable insights about user knowledge and skills
2Adaptability or versatility
If comprehensive user behavior data is collected, then personalized learning can be achieved, but data processing complexity increases
Solution Approach 1:
The patent applies local quality by tailoring learning tools and assessments to each user's specific proficiency levels and behavioral patterns. Instead of uniform processing for all users, the system adapts data analysis and tool generation to match individual learning needs, characteristics, and progress stages
Solution Approach 2:
The patent dynamically changes processing parameters based on user characteristics and data types. Different algorithms and analysis methods are applied depending on the user's proficiency level, behavior patterns, and learning stage, optimizing processing efficiency while maintaining personalization
3Manufacturing precision
If instructors create examinations manually, then question selection can be controlled, but time consumption and bias increase
Solution Approach 1:
The patent enables automated generation of learning tools and examinations using user behavior data and proficiency estimates. The system serves itself by automatically creating personalized assessments and learning materials without requiring manual instructor intervention, significantly reducing preparation time while maintaining quality through data-driven question selection
4Ease of operation
If traditional one-size-fits-all instruction is used, then curriculum delivery is simplified, but student individual needs are not met
Solution Approach 1:
The patent transforms static, fixed curricula into dynamic, adaptive learning paths that automatically adjust based on user performance and behavior. The system continuously updates proficiency estimates and generates personalized learning tools, enabling curriculum delivery to adapt in real-time to each student's evolving needs while maintaining operational simplicity through automation
Data Source
AI summary
There is a computerized learning system and method which updates a conditional estimate of a user signal representing a characteristic of a user based on observations including observations of user behavior. The conditional estimate may be updated using a non-linear filter. Learning tools may be generated using the computerized learning system based on distributions of desired characteristics of the learning tools. The learning tools may include educational items and assessment items. The learning tools may be requested by a user or automatically generated based on estimates of the user's characteristics. Permissions may be associated with the learning tools which may only allow delegation of permissions to other users of lower levels. The learning system includes a method for annotating learning tools and publishing those annotations. In-line text editors of scientific text allow users to edit and revised previously published documents.