Machine-Learning Educational Action Data for Personalized Learning Paths

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Solution Overview

Problem

Online education systems lack personalization and interaction, making it difficult for learners to focus and achieve effective learning outcomes.

Innovation Solution

An apparatus and method using machine-learning to generate educational action data by receiving user data, generating an educational obstacle model, and determining an educational obstacle datum to create personalized educational actions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If online education systems use standardized content delivery, then system complexity is reduced, but personalization and learner engagement deteriorate

Engineering Contradiction:

ImprovepersonalizationVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system automatically generates personalized educational content and determines optimal learning paths using machine learning algorithms that analyze user data without requiring manual intervention from educators, enabling self-service personalization at scale

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system dynamically adjusts educational content parameters (difficulty level, pacing, content type) based on real-time analysis of user performance data, transforming standardized content into personalized learning experiences through parameter optimization

Inventive Principle:

Principle #35Parameter changes

2Reliability

If online education systems increase interaction and personalization, then learner engagement is improved, but data processing requirements and system complexity worsen

Engineering Contradiction:

Improvelearner engagementVSAvoiddata processing requirements

Core Design Contradiction:

ReliabilityVSQuantity of substance

Solution Approach 1:

The system extracts only the most relevant features from user data (performance metrics, engagement patterns, knowledge gaps) to feed into machine learning models, reducing data processing requirements while maintaining personalization quality

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system pre-processes and structures user data in advance using defined schemas and formats, preparing data for efficient machine learning analysis and reducing computational burden during real-time personalization operations

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12573313B2Apparatus and method for generating an educational action datum using machine-learning

Publication Date: 2026.03.10 EDYOU

AI summary

An apparatus and method for generating an educational action datum using machine-learning is described. The apparatus includes at least a processor and a memory containing instructions configuring the at least a processor to receive user data pertaining to a user, generate an educational obstacle machine-learning model using an educational machine-learning module, determine an educational obstacle datum as a function of the user data using the educational obstacle machine-learning model, and generate an educational action datum for the user as a function of the educational obstacle datum, wherein the educational action datum comprises at least an educational action datum waypoint.