Course Graph for Adaptive Educational Content Recommendations
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems lack the ability to provide personalized content recommendations on a large scale, such as across millions of users, with high transaction volumes, and fail to adaptively learn from user interactions to optimize educational content delivery.
Innovation Solution
A computer-implemented method and system that analyzes user interactions with content to generate personalized recommendations by creating a 'course graph' representing modules, concepts, and relationships, which are continually adjusted based on student interactions, allowing for scalable adaptive learning environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If personalized content recommendations are implemented, then user engagement and learning outcomes are improved, but system complexity and computational requirements increase
Solution Approach 1:
The patent segments the content into modular units with defined learning objectives and relationships. The course graph divides the recommendation system into discrete nodes (content items) and edges (relationships), enabling manageable processing of large content repositories while maintaining personalization accuracy through structured analysis of module relationships and user interaction patterns.
Solution Approach 2:
The patent introduces a course graph as an intermediary data structure that mediates between the content repository and the recommendation engine. This graph representation serves as a computational intermediary that simplifies the complex task of analyzing content relationships and user interactions, transforming raw data into structured information that can be efficiently processed for personalized recommendations.
2Productivity
If adaptive learning environments scale to millions of users, then educational impact is improved, but processing time and resource consumption increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing and structuring content into a course graph with defined relationships and learning objectives before user interactions occur. This advance organization enables the system to quickly retrieve and analyze relevant content modules during user sessions, reducing real-time processing requirements and enabling scalable deployment across millions of users without proportionally increasing processing time.
Solution Approach 2:
The patent implements dynamic adaptation where the course graph and recommendations evolve based on user interactions. The system dynamically adjusts the learning path by analyzing interaction data and modifying recommendations in real-time, allowing the infrastructure to efficiently handle varying user needs and scales from individual to millions of users through adaptive rather than static processing.
Data Source
AI summary
Methods, systems and computer program products for providing personalized educational content recommendations are disclosed. A computer-implemented method may include receiving information describing a body of content, receiving data describing an interaction of a user with one or more elements of the body of content, receiving a context that includes one or more criteria associated with the body of content, generating a list of modules from the body of content based on the data describing the interaction of the user with the one or more elements of the body of content in view of the context, and providing the generated list of modules to an interested party.