Adaptive Learning System Task Selection Automation

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Solution Overview

Problem

Existing computer-based learning systems face challenges in adapting to individual user needs, as they often require administrator supervision, which limits accessibility and effectiveness, as users may become frustrated with tasks that are too difficult or bored with those that are too easy, and the system fails to adapt to changing user proficiency or medical conditions.

Innovation Solution

A language, cognition, and skill (LCS) learning system that allows administrators to specify task selection criteria, which the system uses to select tasks for users, overriding criteria if necessary based on user performance, and personalizes content based on user context, location, and preferences to maintain engagement and relevance.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If the system requires administrator supervision to select tasks, then task selection accuracy is improved, but system accessibility and ease of operation deteriorate

Engineering Contradiction:

Improvetask selection accuracyVSAvoidsystem accessibility

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The system enables users to independently select and perform tasks based on their own context, location, and preferences without requiring administrator supervision. The system automatically adapts task selection criteria based on user performance data, allowing users to self-manage their learning activities while maintaining task selection accuracy through automated adaptation mechanisms.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system continuously monitors user performance on tasks and uses this feedback to automatically adjust task selection criteria. This closed-loop feedback mechanism enables the system to maintain accurate task selection without administrator intervention, as the system learns from user performance patterns and adapts the difficulty and type of tasks automatically.

Inventive Principle:

Principle #23Feedback

2Stability of the object's composition

If the system uses fixed task selection criteria specified by administrators, then task selection consistency is improved, but adaptability to individual user needs deteriorates

Engineering Contradiction:

Improvetask selection consistencyVSAvoidadaptability to individual user needs

Core Design Contradiction:

Stability of the object's compositionVSAdaptability or versatility

Solution Approach 1:

The system transforms fixed task selection criteria into dynamic criteria that automatically adjust based on real-time user performance data. The task selection criteria evolve adaptively while maintaining structural consistency, allowing the system to respond to individual user needs without completely abandoning the original selection framework. This enables both consistency and adaptability to coexist.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system modifies task selection parameters such as difficulty level, task type, and complexity based on user performance metrics. By dynamically changing these parameters while maintaining the overall selection framework, the system achieves adaptability to individual users while preserving task selection consistency through the stable underlying criteria structure.

Inventive Principle:

Principle #35Parameter changes

3Ease of operation

If the system presents tasks that are too easy, then user engagement is improved, but learning effectiveness and productivity deteriorate

Engineering Contradiction:

Improveuser engagementVSAvoidlearning effectiveness

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

The system dynamically adjusts task difficulty parameters based on user performance data, automatically modifying task complexity to maintain optimal challenge levels. When users struggle, the system reduces difficulty; when users excel, it increases challenge. This continuous parameter adjustment ensures tasks remain engaging while maintaining learning effectiveness.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system uses real-time feedback from user task performance to automatically adjust subsequent task difficulty. This closed-loop control mechanism ensures that learning challenge and user engagement remain balanced, as the system continuously adapts task parameters based on observed user capabilities and engagement levels.

Inventive Principle:

Principle #23Feedback

4Extent of automation

If the system does not adapt to changing user proficiency, then administrator control is maintained, but learning effectiveness deteriorates

Engineering Contradiction:

Improveadministrator controlVSAvoidlearning effectiveness

Core Design Contradiction:

Extent of automationVSProductivity

Solution Approach 1:

The system automatically monitors and adapts to changing user proficiency levels without requiring administrator intervention. Users benefit from adaptive learning experiences as the system independently adjusts task selection criteria based on performance data, while administrators retain overall system control through configurable parameters and oversight capabilities.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS11600197B2Systems and techniques for personalized learning and/or assessment

Publication Date: 2023.03.07 CONSTANT THERAPY

AI summary

Systems and techniques for personalized assessment and/or learning are provided. The system may select tasks and task content for a user consistent with an administrator's suggested learning regimen for the user, while also adapting the selection of tasks and task content based on the user's performance and/or context when the user is not being supervised by an administrator.