Knowledge Graph for Adaptive Learning Path Generation
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Solution Overview
Problem
Current learning systems for new employees rely on static learning paths that either make assumptions about learners or include redundant content, leading to inefficiencies and inferior learning opportunities due to varying prior knowledge and experiences of new employees.
Innovation Solution
A knowledge graph database system that classifies and links education mediums, allowing for personalized learning paths based on user qualifications, filtering out known content and presenting a curated learning curriculum with chronological order and metadata constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If static learning paths are used for all new employees, then the system is simple to maintain, but the learning experience becomes inferior and redundant for learners with different prior knowledge
Solution Approach 1:
The patent transforms static learning paths into dynamic, adaptive learning paths that automatically adjust based on learner characteristics. The system dynamically generates personalized learning paths by querying the knowledge graph with learner attributes (prior knowledge, experience, context) and retrieving customized sequences of learning objects, thereby resolving the contradiction between system simplicity and individual adaptability.
Solution Approach 2:
The system changes the parameters of learning paths based on learner attributes. By varying parameters such as learning object selection, sequence ordering, and path length according to learner's prior knowledge and experience levels, the system provides customized learning experiences without requiring manual creation of multiple static paths, thus maintaining ease of management while achieving adaptability.
2Adaptability or versatility
If multiple customized learning paths are created for different employee profiles, then the learning experience is optimized for each learner, but the complexity of maintaining these paths increases significantly
Solution Approach 1:
The patent implements a universal knowledge graph structure that serves multiple functions: it stores learning objects, defines relationships between them, and enables generation of multiple customized learning paths from a single unified framework. This universal structure eliminates the need to maintain separate learning path definitions for different learner types, reducing maintenance complexity while preserving full customization capability.
Solution Approach 2:
The knowledge graph acts as an intermediary layer between the learning content repository and the learning path generation process. Instead of directly managing multiple static learning paths, the system uses the knowledge graph to mediate and automatically generate customized paths based on learner attributes, thereby reducing the complexity of path maintenance while achieving high adaptability.
3Reliability
If comprehensive learning content is included in static learning paths, then all learners receive complete coverage, but redundant content is presented to learners who already possess that knowledge
Solution Approach 1:
The patent segments the complete learning curriculum into discrete learning objects within the knowledge graph, each with defined attributes and relationships. This segmentation enables the system to selectively assemble and present only the necessary subset of learning objects for each learner based on their prior knowledge, ensuring complete coverage of required topics while eliminating redundant content and reducing learning time.
4Ease of manufacture
If learning paths assume all employees have the same prior knowledge, then the system is simple to implement, but efficiency is lost due to redundancy for knowledgeable learners
Solution Approach 1:
The system implements self-service functionality where the learning path generation process automatically adapts to each learner's characteristics without requiring manual intervention. The knowledge graph queries learner attributes and autonomously generates customized learning paths, maintaining system simplicity while significantly improving learning efficiency by eliminating redundant content for knowledgeable learners.
Data Source
AI summary
Disclosed herein are systems and methods that can classify a plurality of nodes in a knowledge graph, where each of the plurality of nodes representative of an education medium, establish a plurality of links in the knowledge graph, where each of the plurality of links connects to at least two nodes of the plurality of nodes and each of the plurality of links represents an education relationship between the at least two nodes, receive, from a computing device associated with a user, data representative of qualifications associated with the user, determine a first set of nodes from the plurality of nodes, the first set of nodes representative of education mediums known by the user, create a user path through the knowledge graph, and assign, to each node in the second set of nodes, a chronological identity representative of an order by which the user proceeds through the user path.