Metadata Graph Curriculum Generation for Dynamic Access-Aware Learning
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Solution Overview
Problem
Existing learning curriculum systems rely on static metadata, limiting the ability to dynamically update learning materials based on newly introduced information and user access permissions, leading to suboptimal learning experiences.
Innovation Solution
A method for creating learning curriculums using metadata graphs that filter and generate k-partite graphs based on learning topics and user profiles, dynamically updating the curriculum with dynamically updated metadata and user-specific access permissions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static metadata is used in learning curriculum systems, then system simplicity is maintained, but the ability to dynamically update learning materials based on newly introduced information and user access permissions is limited
Solution Approach 1:
The patent transforms static metadata into dynamic metadata that automatically updates when new information is introduced. The system continuously monitors and updates metadata properties, enabling learning curricula to adapt dynamically without manual intervention, thus resolving the contradiction between adaptability and complexity.
Solution Approach 2:
The system pre-establishes metadata schemas and update rules in advance, so that when new information arrives, the curriculum can be automatically updated following predetermined logic. This preliminary structuring enables dynamic adaptation while maintaining manageable system complexity through standardized processes.
2Measurement precision
If comprehensive metadata filtering and k-partite graph generation are implemented, then user-specific learning experiences are enhanced, but computational processing requirements increase
Solution Approach 1:
The patent divides the metadata graph into k-partite structures, segmenting the comprehensive metadata into distinct partitions based on different criteria (e.g., content type, difficulty level, user role). This segmentation enables targeted filtering and processing of only relevant portions of metadata for each user, improving learning material relevance while reducing overall computational burden through divide-and-conquer processing.
Solution Approach 2:
The system applies different filtering and processing strategies to different partitions of the metadata graph based on local characteristics. Each partition is processed according to its specific properties and the user's needs, rather than applying uniform processing to all metadata, thus optimizing the balance between precision and computational power.
Data Source
AI summary
A method and system for metadata-based learning curriculum generation. A learning curriculum may generally refer to an ordered (or sequenced) manifest of learning materials and/or content that may progressively advance user proficiency in at least a learning topic of interest. Existing systems or solutions offering learning curriculums today rely on the availability of static assets supported by static metadata descriptive thereof. As an improvement over said existing systems/solutions, embodiments disclosed herein enable newly introduced and ingested information, from across various data sources, to dynamically update the metadata and, therefore, dynamically update any learning curriculum(s) contingent on said metadata. Moreover, accessibility or inaccessibility to any given asset (e.g., learning material and/or content) may depend on the access authority granted to the user(s) seeking said given asset.