Sequential Context Media Visualization for Adaptive Learning
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Solution Overview
Problem
Existing content recommendation systems in online education platforms, such as MOOCs, face challenges in providing flexible access to diverse content and perspectives, as they often rely on hierarchical syllabi that professionals may not follow, and manual creation of concept maps is not scalable or adaptive.
Innovation Solution
A method for visualizing and recommending media content based on sequential relationships and content similarity, using a two-dimensional visualization that allows learners to interactively explore recommended videos and navigate multiple perspectives from different courses, supporting semantic visualization and interactive exploration of recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If hierarchical syllabi are used to organize content, then content structure and organization are improved, but flexibility and adaptability for professional learners are reduced
Solution Approach 1:
The system dynamically adjusts the concept map based on user interactions, allowing the structure to evolve from a static hierarchical syllabus to an adaptive visualization that responds to learner behavior. The concept map reorganizes content based on sequential relationships and user preferences, making the system both structured and flexible simultaneously.
Solution Approach 2:
The patent transitions from a one-dimensional hierarchical syllabus structure to a two-dimensional concept map visualization. This dimensional change allows content to be organized both hierarchically and sequentially, providing multiple perspectives for learners to navigate content while maintaining structural integrity.
2Loss of information
If manual concept maps are created by instructors, then content relationships are visualized, but scalability and adaptability are reduced
Solution Approach 1:
The system automatically generates and updates concept maps based on content metadata and user interactions, eliminating the need for manual instructor creation. The concept map self-organizes content relationships based on sequential patterns and user behavior, making the system scalable while preserving content relationship visualization.
Solution Approach 2:
The patent replaces the manual mechanical process of concept map creation with an automated computational system that uses algorithms to detect sequential relationships and generate visualizations. This substitution enables scalability while maintaining the ability to visualize content relationships.
3Adaptability or versatility
If multiple content perspectives are provided, then learner flexibility is improved, but system complexity increases
Solution Approach 1:
The concept map serves multiple functions simultaneously: it visualizes content relationships, provides navigation assistance, recommends next content, and adapts to user preferences. This multi-functionality allows the system to provide multiple content perspectives without proportionally increasing complexity, as a single unified interface handles diverse learner needs.
Data Source
AI summary
A method of visualizing recommended pieces of media content is provided. The method includes identifying at least one piece of media content associated with a received content feature associated with a viewed piece of media content, selecting at least one additional piece of media content linked to the viewed piece of media content by a sequential relationship, generating a two-dimensional visualization based on a content similarity between the identified at least one piece of media content, the viewed piece of media content, and the selected at least one additional piece of media content and the sequential relationship; and displaying the generated two-dimensional visualization.