Automated Student Advising System for Course Recommendation

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Solution Overview

Problem

Higher education institutions, particularly community colleges, face challenges in identifying and supporting students who need academic and personal guidance due to resource limitations and impractical face-to-face counseling methods, leading to inadequate timely intervention and student success issues.

Innovation Solution

A computer-implemented system that collects and processes student data to provide a unified, dynamic platform for academic and personal planning, offering course recommendations, scheduling, and notifications, acting as a self-advising tool to enhance student success by integrating academic, personal, and professional data from multiple sources.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If face-to-face counseling methods are used to provide student guidance, then personalized academic and personal support can be provided, but resource limitations make this approach impractical and inadequate for timely intervention

Engineering Contradiction:

Improvestudent success supportVSAvoidcounseling resource requirements

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system enables students to self-assess their academic standing, course compatibility, and graduation requirements through automated algorithms. The course recommendation engine and degree audit tools allow students to independently plan their academic paths without requiring constant advisor intervention, thus providing reliable support while reducing resource demands.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

An automated advising system serves as an intermediary between students and human advisors. This digital platform processes student data, generates course recommendations, and performs degree audits, freeing human advisors to focus on complex cases while maintaining comprehensive student support at scale.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If comprehensive student data collection is implemented to improve guidance accuracy, then real-time academic and personal planning can be enhanced, but data privacy and security concerns arise

Engineering Contradiction:

Improvestudent guidance accuracyVSAvoiddata privacy risks

Core Design Contradiction:

Measurement precisionVSObject-affected harmful factors

Solution Approach 1:

The system implements differential privacy and data masking techniques where only necessary student information is collected and processed. Sensitive personal identifiers are masked or aggregated, allowing the system to maintain high guidance accuracy through comprehensive data analysis while protecting student privacy through localized data quality control.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system incorporates security measures and privacy protections into the data collection architecture from the outset. Encryption, access controls, and data minimization practices are built-in before data collection begins, cushioning against potential privacy breaches while enabling comprehensive data-driven student support.

Inventive Principle:

Principle #11Beforehand cushioning (Prior cushioning)

3Productivity

If automated course recommendation algorithms are used to improve course selection accuracy, then academic planning efficiency is enhanced, but the complexity of the recommendation system increases

Engineering Contradiction:

Improveacademic planning efficiencyVSAvoidrecommendation system complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The recommendation system is divided into modular components: data collection modules, course compatibility analysis modules, graduation requirement verification modules, and recommendation generation modules. Each module handles a specific aspect of the advising process, making the overall complex system manageable and maintainable while delivering high productivity through automated course selection.

Inventive Principle:

Principle #1Segmentation

4Reliability

If real-time student monitoring and intervention systems are implemented to improve timely support, then student success rates increase, but the system complexity and resource requirements increase

Engineering Contradiction:

Improvetimely student interventionVSAvoidmonitoring system complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system performs periodic automated degree audits and course compatibility checks at key academic milestones (e.g., after each term, before course registration). This periodic monitoring provides timely intervention when students are off-track while avoiding the continuous resource demands of constant real-time monitoring, balancing reliability with manageable system complexity.

Inventive Principle:

Principle #19Periodic action

Data Source

PatentUS11651701B1Systems and methods for processing electronic data to make recommendations

Publication Date: 2023.05.16 EAB GLOBAL INC

AI summary

Systems and methods are disclosed herein for recommending an educational course to a user, and may comprise receiving data records associated with availability of a plurality of educational courses at one or more institutions; receiving educational course data and educational course focus data associated with the user; receiving prior user data records comprising prior user educational course data and prior user educational course focus data; determining index scores for each of the plurality of educational courses based upon a similarity between the educational course data and prior user educational course data, and based upon a similarity between the educational course focus data and prior user educational course focus data; and providing a recommended educational course from the plurality of educational courses to the user based upon the determined index scores.