Degree Matching Algorithm Using Multi-Factor Student Profiles
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Solution Overview
Problem
Current systems fail to provide students with research-informed, multi-factor recommendations for degree and program selection, relying on narrow and superficial inputs, lacking predictive validity for student success and satisfaction, and are not adaptable across different educational institutions.
Innovation Solution
A system that uses machine learning to match students' unique profiles with degree programs based on comprehensive research-informed factors, including socio-emotional and cognitive skills, interests, and aspirations, to optimize decision-making and provide validated recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current systems use narrow and superficial inputs for degree recommendations, then the system complexity is low, but the predictive validity for student success and satisfaction deteriorates
Solution Approach 1:
The system segments the recommendation process into multiple independent components: collecting diverse input data (interests, skills, aspirations), analyzing each factor separately, and synthesizing results into degree recommendations. This segmentation allows comprehensive analysis without overwhelming system complexity.
Solution Approach 2:
The system transitions from one-dimensional inputs (simple preferences) to multi-dimensional analysis by incorporating socio-emotional skills, cognitive skills, interests, and aspirations as separate dimensions. This dimensional expansion enhances predictive validity while maintaining manageable complexity through structured processing.
2Measurement precision
If the system collects comprehensive multi-factor data from students, then the recommendation quality improves, but the data processing complexity increases
Solution Approach 1:
The assessment is divided into distinct modules evaluating different factors (interests, socio-emotional skills, cognitive skills, aspirations). Each module processes specific data types independently, improving measurement precision while keeping individual processing tasks manageable.
Solution Approach 2:
The system transforms raw student responses into standardized parameters and metrics for each assessed factor. This parameter transformation enables precise comparison and analysis of multi-factor data while simplifying the integration process through consistent data formats.
3Reliability
If the system provides detailed multi-factor analysis to students, then the decision-making quality improves, but the information processing time increases
Solution Approach 1:
The system performs preliminary analysis of student data against degree requirements and student success patterns before generating recommendations. This preliminary processing organizes information in advance, enabling faster delivery of quality recommendations without requiring extensive processing time during student interaction.
Solution Approach 2:
The system provides structured feedback to students showing how their multi-factor profile matches recommended degrees. This feedback mechanism delivers comprehensive analysis results efficiently by focusing on actionable insights rather than presenting all raw data, maintaining decision quality while reducing perceived processing time.
Data Source
AI summary
Systems and methods of the present invention provide for: generating a GUI comprising survey questions associated with degree factors and associated rating GUI components indicating application of the factor to a user; receiving the factor rating for each survey question; identifying a high factor rating exceeding a threshold; selecting a degree identifier sharing a common high factor rating between the first response and a response stored in the database; generating a candidate degree list including the degree identifier; generating a second GUI including the candidate degree list; and transmitting the second GUI to a client device.