Skill Proficiency Roadmap System Integrating Learning Data
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Solution Overview
Problem
Organizations face challenges in providing consistent and quality guidance for employees to build proficiencies in skill areas relevant to their business model, as existing learning systems often lack integration of informal learning activities such as job experiences and collaboration, making it difficult for employees to determine how to develop necessary skills.
Innovation Solution
A system that combines job experience, formal learning, and collaborative data to create personalized roadmaps for skill development, using a processor and interface to retrieve and transform relevant data into actionable guides for employees, including recommended job experiences, formal learning activities, and collaborative roles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If learning systems only provide formal learning activities (training courses), then the system structure remains simple, but the completeness of skill development guidance deteriorates
Solution Approach 1:
The patent combines multiple types of learning activities (formal learning, informal learning, job experiences, collaborations) into a single integrated roadmap system. This merging ensures comprehensive skill development guidance while managing system complexity through unified data structures and processing logic.
Solution Approach 2:
The learning system is designed to provide multiple types of guidance (formal courses, informal activities, job experiences, collaboration opportunities) through a single platform. This multi-functionality ensures complete skill development guidance while avoiding the need for separate systems for each learning type.
2Loss of information
If learning systems provide comprehensive guidance including informal learning activities, then the completeness of skill development guidance improves, but the device complexity increases
Solution Approach 1:
The patent segments the learning guidance into distinct categories (formal learning activities, informal learning activities, job experiences, collaborations) while maintaining a unified roadmap structure. This segmentation allows comprehensive guidance to be organized manageably, reducing perceived complexity while maintaining completeness.
Solution Approach 2:
The system uses an intermediary roadmap structure that mediates between various learning activity types and the user. This intermediary layer organizes diverse learning activities (formal and informal) into a coherent guidance path, managing complexity while providing comprehensive information.
3Manufacturing precision
If personalized roadmaps are created for each employee, then the quality of skill development guidance improves, but the processing time and computational resources increase
Solution Approach 1:
The system pre-processes and structures learning activity data into organized frameworks before generating personalized roadmaps. This preliminary organization of formal and informal learning activities, job experiences, and collaborations enables faster personalized roadmap generation while maintaining high quality guidance.
Solution Approach 2:
The system uses parameter-based filtering and selection to efficiently generate personalized roadmaps. By changing parameters such as skill gaps, current competency levels, and preferred learning types, the system can quickly customize guidance without excessive processing time, maintaining quality while reducing computational overhead.
4Manufacturing precision
If the system integrates multiple data sources (job experiences, formal learning, collaborations), then the quality of roadmaps improves, but the data processing complexity increases
Solution Approach 1:
The patent segments data from multiple sources (job experiences, formal learning activities, informal activities, collaborations) into distinct data structures with standardized schemas. This segmentation maintains data integrity and quality while simplifying processing by organizing diverse information into manageable, consistent formats.
Data Source
AI summary
A system is described for providing roadmaps for building proficiencies in skill areas. The system may include a memory, interface, and processor. The memory may store skill area and skill level identifiers, and associated job experience data items describing tasks of a user within the skill area and skill level, formal learning data items describing learning activities of a user within the skill area and skill level, and collaborative data items describing collaborative roles of a user within the skill area and skill level. The processor may receive the skill area and skill level identifiers. The processor may retrieve the job experience, formal learning and collaborative data items associated with the skill area and the skill level identifiers. The processor may transform the data items into a roadmap describing the job experience, formal learning and collaborative data items recommended to reach the skill level, and provide the roadmap to the user.