Personalized Teaching Microskill Rendering System
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Solution Overview
Problem
Existing educational technologies fail to systematically distinguish between the skills of teaching and presenting subject matter in digitized teaching assets, and they do not optimally deconstruct pedagogic skills for personalized instruction in teaching how to teach.
Innovation Solution
The method identifies and dynamically organizes digitized recordings of teaching actions to sequentially present user-selected instances of teaching microskills, using unique identifiers and selecting video models based on specified microskill identifiers, audience characteristics, and information elements, allowing for personalized rendering of teaching capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If digitized teaching assets are made available for viewing via prior art methods, then accessibility and availability of teaching content is improved, but the system fails to systematically distinguish between teaching skills and subject matter presentation
Solution Approach 1:
The patent segments teaching videos into discrete microskills with unique identifiers, separating teaching actions from subject matter content. Each microskill represents a distinct teaching behavior that can be independently identified, analyzed, and retrieved, thereby preserving the distinction between teaching skills and subject matter while maintaining accessibility.
Solution Approach 2:
The patent introduces microskill identifiers as an intermediary layer between the raw video content and the user. These identifiers act as metadata tags that systematically categorize teaching skills, enabling users to access teaching content while simultaneously providing structured information about the teaching skills demonstrated, thus preventing loss of distinction.
2Device complexity
If teaching skills are addressed in the aggregate, then simplicity of the system is maintained, but optimal deconstruction of pedagogic skills for personalized instruction is not achieved
Solution Approach 1:
The patent divides aggregate teaching skills into granular microskills, each with a unique identifier. This segmentation enables personalized instruction by allowing users to select and view specific microskills relevant to their needs, transforming a simple but non-personalizable system into a versatile yet systematically organized platform.
3Quantity of substance
If all teaching video models are made available without selection, then completeness of teaching resources is improved, but the ability to provide personalized instruction based on user needs is reduced
Solution Approach 1:
The patent performs preliminary organization of teaching videos by assigning microskill identifiers to each video model before user access. This pre-categorization allows the system to maintain completeness of resources while enabling personalized retrieval, as users can selectively access videos based on their specific learning needs without being overwhelmed by the full collection.
Solution Approach 2:
The system incorporates user feedback mechanisms that track which microskills users view and master. This feedback loop enables progressive personalization, where the system learns from user interactions and can recommend or prioritize specific microskills, thereby enhancing personalization while maintaining resource completeness.
Data Source
AI summary
A system and method for characterizing, selecting, ordering and rendering discrete elements of digitized video content to teach communications and pedagogic skills. Each of a plurality of observed or computer-generated instances of modeling of distinguishable teaching skills are recorded as digitized assets. Microskills are identified and deconstructed in the abstract from one or more of the visual and audible recordings of teaching skills modeling moments. Identifiers of microskills are associated by a human editor with recorded modeling instances and/or portions thereof. Modeling presentations are dynamically generated by a user asserting one or more microskill identifiers and a network-enabled selection, ordering and rendering of portions of modeling instances that are associated with the asserted microskill identifiers.