Automated Competency Gap Analysis Using Hybrid Matching and LLMs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Manual gap analysis in educational courses leads to human error and missed key competencies, failing to align course design with evolving job market demands, and lacks insights into specific skill demands.
Innovation Solution
A computer-implemented method using a hybrid approach combining rule-based and similarity-based matching, along with a pre-trained large language model (LLM), to extract and map job and course competencies, generating a recommended course dataset for improved alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual gap analysis is performed by course administrators, then human judgment and flexibility are applied, but human error and missed key competencies occur leading to suboptimal course design
Solution Approach 1:
The system enables automated self-service gap analysis by extracting competencies from job postings and course syllabi, comparing them automatically, and generating recommendations without requiring manual administrator intervention for the core analysis tasks
Solution Approach 2:
The patent replaces the manual mechanical process of gap analysis with an automated computational system using NLP and machine learning models to extract, compare, and analyze competencies, eliminating human error while maintaining analytical depth
2Productivity
If manual gap analysis is performed infrequently, then resource consumption is reduced, but the analysis becomes outdated and misses evolving job market competencies
Solution Approach 1:
The system enables continuous automated gap analysis that can run repeatedly and frequently, continuously monitoring job market changes and updating course recommendations to ensure competencies remain current with evolving industry needs
Solution Approach 2:
The automated system performs comprehensive analysis of all job postings and syllabi, potentially analyzing more data than manual processes could handle, ensuring no key competencies are missed while maintaining efficiency through algorithmic processing
3Loss of information
If manual gap analysis is performed, then flexibility in interpretation is maintained, but insights into relative demand for specific skills are not provided
Solution Approach 1:
The system segments the gap analysis into distinct modular components: competency extraction from job postings, competency extraction from syllabi, competency matching and comparison, demand analysis, and recommendation generation. This modular architecture manages complexity while enabling comprehensive analysis
Solution Approach 2:
The patent introduces an intermediary automated analysis system that bridges job market data and course design, using NLP models and comparison algorithms to translate raw data into actionable insights about skill demand and gap identification
Data Source
AI summary
There is provided a computer-implemented method for automated gap analysis that performs the steps of: generating a job competencies dataset, generating a course competencies dataset; generating a missing competencies dataset based on the job competencies dataset and the course competencies dataset, and outputting a recommended course dataset. The method identifies job competencies missing in course competencies, and recommends courses in which to include the missing competencies. The approach to competency identification comprises either a hybrid approach composed of rule-based matching and/or similarity matching, or a pre-trained large language model (LLM).