Event Recommendation System for Course Selection
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Solution Overview
Problem
Students in higher education institutions face challenges in selecting the best courses due to numerous options, with the best choice often being non-transparent, and must interact with multiple systems and documents for tasks like scheduling and billing.
Innovation Solution
An event recommendation system using a machine-learning algorithm that recommends courses or classes based on the characteristics of the candidate attendee, comparing them to prior attendees' experiences, to suggest courses where similar students have performed well and avoid those where similar students have struggled.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If students interact with multiple systems and documents to complete tasks like scheduling and course selection, then they can access comprehensive information, but the complexity of the process increases and decision-making becomes more difficult
Solution Approach 1:
The patent combines multiple separate systems (course catalog, scheduling system, billing system, academic advising) into a single integrated event recommendation system. This consolidation maintains comprehensive information access while reducing process complexity by providing a unified interface and automated recommendation engine that processes data from all systems simultaneously.
Solution Approach 2:
The virtual assistant acts as an intermediary between students and the complex institutional systems. It translates student needs into system queries and presents processed recommendations, shielding students from the underlying complexity while maintaining access to comprehensive information through natural language interaction.
2Adaptability or versatility
If students are presented with many alternative courses, then they have more options to choose from, but the best choice becomes less transparent and harder to identify
Solution Approach 1:
The system implements feedback loops that analyze student characteristics, performance data, and course outcomes to generate personalized recommendations. This feedback mechanism processes comprehensive course information and presents it in prioritized, transparent recommendations with explanations of why each course is suggested, making the best choices visible despite the large number of options.
Solution Approach 2:
Instead of presenting all course options uniformly, the system applies local quality by tailoring the presentation of course information to each student's specific characteristics, goals, and performance history. Each student receives a customized view of course options with differentiated information highlighting the most relevant choices for their individual situation.
3Speed
If a virtual assistant executes simple tasks responsive to requests, then it provides quick responses, but it cannot handle complex decision-making like course selection
Solution Approach 1:
The virtual assistant dynamically adapts its processing depth based on task complexity. For simple queries, it provides immediate responses maintaining high speed. For complex course selection tasks, it activates the machine learning recommendation engine that processes comprehensive data, balancing response time with decision-making capability through dynamic resource allocation.
Solution Approach 2:
The virtual assistant serves as an intermediary that routes simple tasks for quick execution while directing complex decision-making tasks to the sophisticated recommendation engine. This layered approach maintains quick response times for routine queries while enabling comprehensive analysis for complex course selection, effectively bridging the gap between speed and versatility.
Data Source
AI summary
An event recommendation system recommends events for a candidate attendee. The system recommends an event based on characteristics of the candidate attendee and characteristics of prior attendees that attended a prior occurrence of the event. A prior attendee may have a positive or negative experience with the prior occurrence of the event. A positive experience may be defined, for example, as enjoying the event, completing the event, or performing well in the event. A negative experience may be defined, for example, as not enjoying the event, not completing the event, or not performing well in the event. An event is recommended to a candidate attendee if the candidate attendee has similar characteristics to a prior attendee that had a positive experience. An event is not recommended to a candidate attendee if the candidate attendee has similar characteristics as a prior attendee that had a negative experience.