Financial-Aware Graduation Prediction System Using Segmented Data Adapters
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Solution Overview
Problem
Higher education institutions face challenges in integrating disparate data sources for student advising and retention due to archaic ERP systems, limited reporting capabilities, and technical issues, leading to ineffective student support and low graduation rates, particularly for Pell Grant recipients.
Innovation Solution
A flexible, integrated financially aware graduation outcome prediction system using predictive analytics and machine learning to integrate multisource student data, providing actionable insights and intervention tools, including intelligent chatbots for academic and financial planning, to support students and advisors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If predictive analytics and machine learning are used to integrate multisource student data, then graduation outcome prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the complex integration task by creating separate data adaptation layers for different source systems (ERP, financial aid systems, academic systems). Each data source is integrated through its own adapter module, which transforms and standardizes data before feeding it into the predictive analytics engine. This modular segmentation reduces overall system complexity while maintaining high prediction accuracy.
Solution Approach 2:
The patent introduces intermediary components including a data adaptation layer and API gateway that mediate between disparate data sources and the machine learning models. These intermediaries handle data transformation, validation, and standardization, allowing the predictive analytics to access integrated student data without direct complexity from underlying system variations.
2Loss of information
If real-time data integration from multiple sources is implemented, then actionable insights are improved, but data integration difficulty increases
Solution Approach 1:
The system implements a universal data adaptation layer that handles multiple data sources (academic systems, financial aid systems, ERP systems) through a single standardized interface. This multi-functional adapter framework can accommodate different data formats and sources without requiring separate integration logic for each system, reducing integration difficulty while maintaining real-time actionable insights.
Solution Approach 2:
An API gateway and data adaptation layer serve as intermediaries between disparate data sources and the predictive analytics engine. These intermediaries real-time transform and standardize data from multiple sources, enabling actionable insights without direct complexity from underlying system variations.
3Reliability
If comprehensive student data is integrated across data silos, then student success support is improved, but integration cost increases
Solution Approach 1:
The system segments the data integration architecture into modular components: separate data adapters for different systems, a central data lake, and predictive analytics modules. This segmentation allows institutions to implement integration incrementally and reduces overall integration cost by reusing standardized adapter patterns across different data sources.
Solution Approach 2:
The data adaptation layer and API gateway act as intermediaries that enable comprehensive data integration without direct system-to-system connections. This intermediary approach reduces integration cost by providing a standardized translation layer that avoids expensive custom point-to-point integrations between disparate systems.
Data Source
AI summary
A model trained with student-specific academic data, student-specific financial data, institutional policy data, and student-specific outcomes is provided. Subject student-related academic data and subject student-related financial data are applied to the model to generate advisor-facing metrics and/or student-facing metrics relating to student progress, such as a financial estimate pertaining to completion of a degree, a predicted student success indicator, and/or the like. A student-facing user interface and advisor-facing user interface facilitates configuration and collaboration of a student-specific academic plan, and intervention by advisor-users. The model is routinely updated and trained online, and an administrator-facing interface enables configuration per institution. Users are notified of alerts or changes in predicted outcomes. The model may include a large language model to facilitate natural language interaction and/or feedback. The system addresses security, privacy, system integration, and customization needs of higher education institutional systems.