Content Affinity Analytics Graph Database for LMS Data Exchange
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Solution Overview
Problem
Current Learning Management Systems (LMS)/Content Management Systems (CMS)/Authoring Management Systems (AMS) lack methods for analyzing content reusability and predicting future usage of content, and there is no protocol for exchanging such data between systems, limiting the ability to trace learner concept knowledge and content organization effectively.
Innovation Solution
A content affinity analytics system that captures learning data from LMS into a relational database, filters and transforms it into a graph database, where an affinity analytics process establishes relationships between learning nodes, enabling the generation of affinity insights and recommendations, and facilitates data exchange across multiple systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If content categorization and searching is done in a streamlined fashion using existing LMS/CMS/AMS systems, then the ease of operation is improved, but the ability to perform reusability study and predictive analysis is lost
Solution Approach 1:
The patent introduces an intermediary data exchange protocol and analytics engine that sits between the content management systems and the user interface. This intermediary layer captures usage data, performs affinity analysis, and generates reusability insights without disrupting the streamlined content categorization and searching operations. The protocol acts as a mediator that extracts valuable information while maintaining system simplicity.
2Measurement precision
If a graph database is used to establish relationships between learning nodes for affinity analytics, then the measurement precision of content relationships is improved, but the device complexity increases
Solution Approach 1:
The patent segments the data storage and processing system into distinct modular components: a graph database module for relationship storage, an affinity analytics engine for processing, and a protocol layer for data exchange. This segmentation allows the system to implement complex graph-based relationship tracking while maintaining manageable system architecture through clear separation of concerns and independent module deployment.
3Adaptability or versatility
If learning data is captured from multiple LMS systems and transformed into graph form, then the adaptability of the system is improved, but the loss of time for data transformation increases
Solution Approach 1:
The patent implements preliminary action by pre-defining data schemas, relationship templates, and transformation rules before data ingestion. The system prepares graph structure templates and affinity analysis configurations in advance, allowing incoming learning data from multiple LMS systems to be quickly mapped and transformed without ad-hoc processing delays. This pre-preparation significantly reduces real-time transformation time while maintaining multi-system adaptability.
Data Source
AI summary
Method and system for content affinity analytics using graph and exchange of affined learning data across learning management systems (LMS), are provided. The present invention relates to a method and system for performing content affinity analytics in learning management systems such as assessment management systems (AMS)/content management systems (CMS)/learning management system (LMS). The present invention comprises of a data capture service layer, affinity analysis layer and an information retrieval layer. The data capture service Layer models the data management entities into a connected graph. The affinity analysis layer identifies the affinity data and creates/updates the relations among the nodes. Further, the information retrieval layer exposes APIs which queries the graph and returns data in a JSON format.