Adaptive Learning System Using NLP Content Tagging

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Solution Overview

Problem

Existing eLearning systems require significant manual effort for individualization, making it labor-intensive and cumbersome to adapt learning content based on student behavior and performance, which complicates the authoring and teaching processes.

Innovation Solution

A method and system that automates content tagging and individualization decisions using behavioral data, employing Natural Language Processing (NLP) and machine learning to adjust the course delivery sequence in real-time, based on student interactions, preferences, and learning styles, without requiring upfront human input.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If manual individualization methods are used to customize learning content based on student behavior, then learning personalization is improved, but instructor workload and system complexity increase significantly

Engineering Contradiction:

Improvelearning personalizationVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system automatically collects student behavioral data, analyzes performance patterns, and generates personalized learning paths without requiring manual instructor intervention. The system serves itself by using machine learning algorithms to process data and make adaptation decisions, eliminating the need for instructors to manually tag content or define transition logic.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Manual mechanical processes of content tagging and rule definition are replaced with automated computational systems. Machine learning models and data analytics replace the manual cognitive work of instructors, transforming the system from a manually-operated adaptation mechanism to an automated intelligent system.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If manual content tagging and rule definition are required for course adaptation, then individualization capability is improved, but authoring time and effort increase

Engineering Contradiction:

Improvecourse adaptation capabilityVSAvoidauthoring time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system pre-processes course content during authoring by automatically tagging content with metadata and organizing it into structured formats. This preliminary automated action prepares the content for future adaptation without requiring manual tagging during the authoring process, saving time when courses are later customized for individual students.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system automatically generates adaptation rules and learning paths based on pre-collected behavioral data and performance metrics, eliminating the need for authors to manually define transition logic. The system serves itself by using its own data to drive adaptation decisions.

Inventive Principle:

Principle #25Self-service

3Measurement precision

If comprehensive behavioral data collection is implemented to improve individualization accuracy, then learning efficacy is improved, but data processing complexity and computational requirements increase

Engineering Contradiction:

Improvestudent performance measurement accuracyVSAvoiddata processing complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system extracts only the most relevant behavioral features and performance metrics from comprehensive data collections, focusing on key indicators that drive adaptation decisions. By selecting and extracting only essential data elements, the system maintains high measurement precision while reducing processing complexity to manageable levels.

Inventive Principle:

Principle #2Taking out (Extraction)

4Productivity

If real-time adaptation based on student interactions is implemented, then learning efficiency is improved, but computational processing requirements increase

Engineering Contradiction:

Improvelearning efficiencyVSAvoidcomputational energy consumption

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The system implements real-time adaptation selectively, focusing computational resources on critical decision points and key learning moments rather than continuously processing all student interactions. By applying adaptation partially at strategically important moments, the system maintains learning efficiency while managing computational energy consumption.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS11443648B2Systems and methods to assist an instructor of a course

Publication Date: 2022.09.13 ZOOMI

AI summary

A system and method for delivery of an online course, customized to a student based on captured student's actions in interacting with the course is disclosed. The actions are compared to actions of others, where the actions of others are correlated with known learning results. In part, such comparison involves tagged content and the tagging process is also a part of the present invention.