Learning Graph for Dynamic Content Adaptation
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Solution Overview
Problem
Traditional learning management systems are restrictive, static, and inefficient in providing customized learning content to employees, as they are limited by high computational costs and inability to adapt to rapid changes in trends and individual needs, leading to obsolete content and inadequate employee skill development.
Innovation Solution
A computer automated learning management system utilizing a learning graph that represents users, content, and learning goals, allowing for dynamic and personalized content recommendations through algorithms and statistical analysis, enabling users to discover necessary learning paths and content automatically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional learning management systems are used to provide customized learning content, then employee skill development is addressed, but computational costs become prohibitively high and content becomes obsolete quickly
Solution Approach 1:
The patent uses graph copying techniques where a subgraph representing an employee's learning context is extracted and copied to generate personalized learning paths. This avoids processing the entire learning database for each employee, significantly reducing computational costs while maintaining customization. The system copies relevant portions of the learning graph rather than performing full-scale analysis.
Solution Approach 2:
The learning management system divides the large-scale learning database into manageable subgraphs based on employee roles, departments, and learning objectives. By segmenting the data into smaller, relevant portions, the system reduces the computational burden while still providing comprehensive customized learning content. Each subgraph can be processed independently and efficiently.
2Loss of information
If internal content is produced and maintained for learning systems, then content relevance to organizational needs is improved, but production costs increase and content becomes obsolete fast
Solution Approach 1:
The patent implements a multi-source content strategy where the learning system universally accepts and integrates content from multiple origins including internal organizational data, external educational platforms, and third-party resources. This universal content acquisition approach eliminates the need for expensive internal content production while maintaining high relevance through the graph-based matching system that connects any content source to appropriate employees based on their learning needs and organizational context.
3Adaptability or versatility
If employees are provided with learning content through traditional systems, then basic training needs are met, but the system cannot easily adapt to new trends and individual needs
Solution Approach 1:
The patent implements a dynamic learning graph that automatically updates and reconfigures based on new organizational data, emerging trends, and individual employee progress. The system continuously adapts the learning paths by dynamically adjusting the graph structure, adding new nodes for emerging skills or trends, and reweighting connections based on current organizational priorities. This dynamic nature allows rapid response to new trends without requiring complex manual system reconfiguration.
4Ease of operation
If computational analysis is performed to identify relevant learning content for each employee, then personalized learning paths are created, but computational costs increase and meaningful results are uncertain
Solution Approach 1:
The patent pre-processes and structures organizational learning data into graph formats during off-peak times, organizing content, skills, and relationships into ready-to-query subgraphs. This preliminary action ensures that when personalized learning paths need to be generated, the system only needs to perform lightweight matching operations on pre-organized data rather than conducting expensive real-time analysis. The meaningful results are achieved through this efficient pre-computation approach.
Data Source
AI summary
The present disclosure includes techniques pertaining to computer automated learning management systems and methods. In one embodiment, a system is disclosed where information is represented in a learning graph. In one embodiment, a framework may be used to access different algorithms for identifying customized learning content for a user. In another embodiment, the present disclosure includes techniques for analyzing content and incorporating content into an organizational glossary.