Predictive Learner Score for Skill Gap Analysis
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Solution Overview
Problem
Organizations and educational institutions face challenges in identifying suitable employees and students for desirable careers due to limited access to evidence-based analytics, often relying on anecdotal evidence and personal preferences rather than data-driven insights.
Innovation Solution
A system and method for predictive learner scoring that correlates student profiles with role success models, using machine learning models to match student skills with required skills for specific roles, providing actionable insights for skill development and career pathway recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If organizations and educational institutions rely on anecdotal evidence and personal preferences to identify suitable employees and students, then the decision-making process is simple and quick, but the accuracy and reliability of selection are poor
Solution Approach 1:
The patent introduces predictive analytics models and data processing systems as intermediaries between raw student data and hiring decisions. These intermediaries transform unstructured anecdotal evidence into structured, quantifiable predictions of employee performance, thereby improving selection accuracy without requiring decision-makers to directly analyze complex raw data
Solution Approach 2:
The patent replaces manual, intuition-based selection processes with automated predictive analytics systems. Machine learning models and statistical algorithms substitute for human judgment in evaluating candidate suitability, objective criteria replace subjective personal preferences, and data-driven insights replace anecdotal evidence, thereby improving measurement precision while managing complexity through automation
2Adaptability or versatility
If educational institutions design offerings based on perception of employer needs rather than evidence-based analytics, then the design process is flexible and responsive to perceived market demands, but the alignment with actual employer needs is insufficient
Solution Approach 1:
The patent performs preliminary predictive analytics on student outcomes and employment success before designing educational offerings. By analyzing historical data and predicting future performance, the system identifies which educational programs and curricula are most likely to produce employees who meet actual employer needs, allowing institutions to proactively align their offerings with market demands based on evidence rather than perception
Solution Approach 2:
The patent implements feedback loops where predictive analytics continuously monitor the alignment between educational offerings and employer needs. The system collects data on student performance, employment outcomes, and employer satisfaction, then uses this feedback to refine and adjust educational program design, ensuring ongoing adaptability and evidence-based decision-making
Data Source
AI summary
Embodiments are directed to managing data correlation over a network. Role success models that correspond to roles and to success criteria may be provided. A student profile that includes skill vectors may be provided based on student information. Role success models may be employed to determine intermediate scores based on the skill vectors and the success criteria. A predictive score for the student that corresponds with a predicted performance of the student in the roles may be generated based on the one or more intermediate scores. Actions for the student may be determined based on a mismatch of the skill vectors and role skill vectors that correspond to the roles. In response to the student performing the actions: updating the one or more skill vectors based on a completion of the actions; and updating the predictive score based on the role success models and the updated skill vectors.