Curriculum Graph Learning Paths for Adaptive Concept Retrieval
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Solution Overview
Problem
Traditional educational systems struggle to adapt to diverse learning needs and styles, lack flexibility in integrating various educational materials, and provide limited retrieval of educational content, leading to fragmented learning experiences.
Innovation Solution
A curriculum graph database system that generates dynamic curriculum graphs using a graph database, parsing curriculum data to identify concepts, mapping learning resources, and creating personalized learning paths based on user inputs, leveraging natural language processing and machine learning to optimize the educational journey.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional linear curricula are used, then content delivery is simple and structured, but learning experiences become fragmented and fail to capture connections between concepts
Solution Approach 1:
The patent transforms static linear curricula into dynamic curriculum graphs where concepts are represented as nodes and relationships as edges. The system dynamically generates learning paths based on student progress, allowing the curriculum structure to adapt and reconfigure itself rather than following a fixed sequence.
Solution Approach 2:
The patent adds a dimensional transformation by representing curriculum content as a graph structure with multiple dimensions - conceptual relationships, prerequisite dependencies, and resource associations - rather than a single linear sequence. This enables multi-path navigation through learning content.
2Adaptability or versatility
If conventional educational platforms are used, then system structure is simple and rigid, but flexibility to integrate various educational materials and adapt to evolving needs is limited
Solution Approach 1:
The patent creates a universal curriculum graph framework that can represent and integrate multiple types of educational resources (videos, articles, exercises, assessments) through a common graph structure. The system handles diverse resource types uniformly through standardized node and edge representations.
Solution Approach 2:
The patent introduces curriculum graphs as an intermediary layer between educational resources and learning management functions. This graph structure mediates between raw educational materials and student learning paths, enabling flexible integration without direct coupling between resources and delivery mechanisms.
3Productivity
If basic search and navigation functionality is provided, then system complexity is low, but retrieval of educational content is limited and inefficient
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors student progress through assessments and learning activities, then uses this feedback to dynamically adjust and update learning paths. The curriculum graph evolves based on student performance data, providing adaptive content retrieval.
Solution Approach 2:
The patent performs preliminary action by pre-computing and storing curriculum graphs that map relationships between all educational concepts and resources. This pre-processing enables efficient query execution and learning path generation without requiring complex real-time computations during student interactions.
Data Source
AI summary
A curriculum graph database environment includes a curriculum graph database providing access to granular concepts covered in one or more curriculum. A user accesses the graph database via a user interface of an online learning platform. The graph database includes a curriculum graph generator that parses one or more curriculum data to identify a plurality of concepts, where each concept represents a concept node. The curriculum graph generator maps one or more learning resources included in the graph database to one or more related concept nodes. The user provides his inputs to the graph database, via the user interface, to retrieve a learning path related to a selected learning topic that he wants to master. The graph database includes a learning path generator that generates a learning path including list of concept nodes and associated learning resources to be completed by the user to attain mastery in the selected topic.