Cognitive Content Mapping for Automated Course Gap Detection
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Solution Overview
Problem
The labor- and time-intensive process of mapping existing instructor-led training (ILT) courses to electronic learning (e-learning) courses, which relies heavily on the expertise of instructional designers, is inefficient and lacks automation in identifying content gaps and integrating additional resources.
Innovation Solution
A cognitive content mapping and collating method that partitions courses and resources into consumable modules, detects content coverage gaps through semantic comparison, and automatically generates work items to update the courses by incorporating external resources, using structured metadata and multimedia analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual mapping process is used by instructional designers, then content mapping accuracy is improved, but labor intensity and time consumption increase
Solution Approach 1:
The patent segments the course mapping process into distinct automated stages: content extraction from source courses, semantic analysis using NLP techniques, gap detection through comparison algorithms, and recommendation generation. This segmentation allows computational automation of routine tasks while maintaining accuracy through structured processing of course materials and learning objectives.
Solution Approach 2:
The patent introduces an intermediary automated mapping system that acts as a bridge between instructional designers and course content. This intermediary performs preliminary mapping analysis, gap detection, and recommendation generation, reducing the time burden on designers while maintaining mapping quality through algorithmic content comparison and semantic analysis.
2Reliability
If manual mapping process is used by instructional designers, then mapping quality is improved, but productivity decreases
Solution Approach 1:
The patent performs preliminary automated actions including content extraction, semantic tagging, and preliminary mapping analysis before human designers review the results. This preliminary processing prepares structured data and identifies obvious gaps, allowing designers to focus on quality assurance and complex judgment tasks, thereby increasing overall productivity without sacrificing mapping quality.
Solution Approach 2:
The patent implements feedback mechanisms where the automated system generates mapping recommendations and gap analyses that are reviewed and refined by instructional designers. The system learns from designer corrections and adjustments, improving mapping quality over time while maintaining high throughput through iterative refinement rather than complete manual rework.
3Productivity
If automated content analysis is implemented, then productivity is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal automated mapping system that handles multiple course formats, content types, and mapping scenarios through a single integrated platform. The system performs content extraction, semantic analysis, gap detection, and recommendation generation across diverse training materials, reducing the need for separate specialized tools and simplifying the overall system architecture despite the complexity of individual functions.
4Measurement precision
If semantic comparison is used for gap detection, then mapping precision is improved, but computational requirements increase
Solution Approach 1:
The patent applies semantic comparison selectively to specific course segments, learning objectives, and content gaps rather than performing exhaustive full-course analysis. The system identifies and focuses computational resources on areas with detected discrepancies or high-importance learning objectives, maintaining gap detection accuracy while reducing overall computational requirements through targeted local analysis.
Data Source
AI summary
Methods, systems, and computer program products for cognitive content mapping and collating are provided herein. A computer-implemented method includes identifying resources relevant to an existing course; partitioning, based on pre-determined partitioning parameters, (i) the existing course into multiple portions and (ii) the resources into multiple portions; detecting content coverage gaps in the existing course by semantically comparing (i) the multiple portions of the existing course with (ii) the multiple portions of the resources; retrieving, based on the detected content coverage gaps, at least one of the multiple portions of the resources; and generating an updated version of the existing course by incorporating the at least one retrieved portion of the resources into the existing course.