"Optimized Student Grouping for Enhanced Classroom Performance" by Kathryn E. Reardon
 

Honors Theses

Date of Award

Spring 5-9-2025

Document Type

Undergraduate Thesis

Department

Computer and Information Science

First Advisor

Yixin Chen

Second Advisor

Allan Bellman

Third Advisor

Timothy Holston

Relational Format

Dissertation/Thesis

Abstract

Effective grouping methods enhance classroom collaboration and allow for a student-centered teaching approach; however, traditional grouping methods are time-consuming, subjective, and can create inconsistent group dynamics. This project addresses these challenges by employing a data-driven approach to optimize student groups based on academic performance, behavior, attendance, language barriers, and teacher preferences. The minimum viable product is a web application with an algorithm-driven system to group students and a database storage for group results. During the initiation phase, a problem was defined with a proposed solution. During the planning phase, potential design choices and grouping methods were researched and assessed. During the development phase, the web application with the grouping algorithm was developed. The final phase was the closing phase where the product was delivered with a final report and oral presentation. These phases were completed in conjunction with status report updates to develop a successful minimum viable product.

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