top of page


Also known as the Matching And Grouping of Interests in Capstone for the University of Nebraska—Lincoln's School of Computing. Split up into 3 Releases, Our team of non-Computer Science majors tackled a software development project diving deep into Agile methodologies and using technologies such as React.js, Python, and Flask to create an API. The end goal was to deliver a minimum viable product.

Role: Product Manager, Developer

Release 1


Faculty and staff typically spent over 7 hours assigning students to sponsored projects based on their project preferences, major, leadership preferences, prerequisite performance, teammate preferences, and the preferences of the Senior Design student-base overall. This criteria spans across three files which are referenced during the assignment process.


User Analysis

Typically, faculty and staff handle the student assignments. Particularly involved in the Senior Design Capstone program for the School of Computing. These users consist of around middle aged users with high technological knowledge.

Story Map

Screen Shot 2022-05-10 at 3.22.10 PM.png
Screen Shot 2022-05-10 at 3.22.18 PM.png


A potential solution was to create a web application that allows users to submit the necessary files needed for the matching to occur. Instructions and templates would be provided on the page.


Low Fidelity Wireframes

Screen Shot 2022-05-10 at 3.10.29 PM.png

Release 2


Using Zenhub, our tasks were estimated, assigned, and describes using user stories. Each card was labeled with their respective sprint, epic, and release. I began developing the frontend for the three files to be submitted using React.js and Flask.

Screen Shot 2022-05-10 at 3.36.15 PM.png

Initial User Interface


Release 3


This release was spent improving, documenting, and transitioning the appropriate parties for the product. The algorithm was finished and the minimum viable product was given to the School of Computing. This product will be used moving forward as the Senior Design program assigns students to sponsored projects.


Results of Our Algorithm:

  • 96% of students received a project within their Top 5

  • 74% of students received a project within their Top 3

  • 44% of students received their #1 ranked project

How does MAGIC Work?

The frontend will take the appropriate files and run through an algorithm which then sorts students. The three files are first combined and then sorted with the rest of the code. The outcome provides a CSV with students assigned to their sponsored project.


bottom of page