Contextualized Knowledge Base for Personalized Content Recommendation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing online learning management systems fail to provide content to learners in a structured and personalized manner, neglecting factors such as learning adaptability, prior knowledge, time constraints, and behavioral traits, which limits the effectiveness of content recommendation.
Innovation Solution
A system and method that utilizes a processor to identify a subset of a knowledge base based on a learner's academic and behavioral context, structures content based on a learning strategy, and recommends it using a prioritization module, considering factors like academic ability, effort, and predisposition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional online learning management systems provide content based only on learner's explicit actions or requests, then the system operation is simple, but the personalization and effectiveness of content recommendation is insufficient
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring learner interactions with content and using this information to refine future recommendations. The processor analyzes learner responses, engagement patterns, and performance data to dynamically adjust content selection, creating a closed-loop system that improves personalization over time while maintaining operational simplicity.
Solution Approach 2:
The patent introduces an intermediary processing layer between the content repository and the learner. This intermediary processor analyzes multiple learner attributes (explicit actions, implicit behaviors, performance metrics) and mediates content selection by matching learners with appropriate content subsets, thereby enhancing personalization without requiring complex direct learner-content matching.
2Measurement precision
If the system considers multiple learner factors (learning adaptability, prior knowledge, time constraints, behavioral traits), then the personalization accuracy is improved, but the system complexity increases
Solution Approach 1:
The system segments the content repository into multiple subsets organized by different criteria (topic, difficulty level, format, duration). The processor selectively draws from these pre-segmented subsets based on learner characteristics, avoiding the need to evaluate all content items individually. This segmentation enables accurate personalization while maintaining manageable system complexity through modular content organization.
Solution Approach 2:
The system employs parameter changes by dynamically adjusting content selection based on varying learner parameters (prior knowledge level, available time, learning style preferences). The processor modifies content attributes such as difficulty, length, and format to match learner parameters, achieving high personalization accuracy through systematic parameter matching rather than complex system architecture.
3Ease of manufacture
If the system provides unstructured content recommendations, then the implementation is straightforward, but the learning outcomes and learner engagement are reduced
Solution Approach 1:
The system performs preliminary actions by pre-organizing content into structured subsets with defined attributes (topic categories, difficulty levels, estimated completion time, format types). This pre-structuring occurs before content delivery, enabling the processor to quickly assemble personalized learning paths without complex real-time processing, thereby maintaining implementation simplicity while improving learning outcomes through systematic content organization.
Solution Approach 2:
The patent implements dynamics by creating adaptive learning paths that adjust content sequence and selection based on learner progress and performance. The system dynamically restructures content delivery plans as learners interact with materials, modifying subsequent recommendations to optimize learning outcomes. This dynamic restructuring is achieved through rule-based logic that maintains implementation simplicity while delivering personalized structured content.
Data Source
AI summary
A system for recommending content for use, by a learner is provided. The system includes a processor in communication with a memory. The memory stores a knowledge base. The processor is configured for identifying a subset of the knowledge base, based on the academic context of the learner and for identifying a first set of content tagged to interlinked nodes of the subset of knowledge base. The processor is configured for identifying a second set of content from the first set of content, based on the behavioral context and the characteristics of the learner. The processor is configured for identifying a learning path for the learner, based on a learning strategy for the learner and a score assigned to a concept. The processor is configured for structuring the identified second set of content for recommending to the learner, based on the identified learning path.