Galvanize Groups

Group up at http://galvanizegroups.bxzcwumakq.us-east-2.elasticbeanstalk.com/

Project Motivation

• Educators have countless tasks to complete and very limited time to do so.
• This project aims to tackle the very important (and very time consuming) task of effectively grouping students.
• Providing educators with a simple tool to create groups of students using insights driven by data can help them spend more time planning engaging lessons and creating personalized education experiences.

Generating Clusters From Assessments

Clustering Inputs

CSV File

Educators  upload a CSV file with student names and assignment scores

Number of Clusters

Choose between 2-9 clusters to break students into

How it Works

KMeans Clustering

The KMeans Clustering algorithm seeks to create clusters of similar students using student assessment data.

It does this through randomly assigning a fixed number of cluster centroids and iteratively associating the remainder of the data points to the nearest centroid.

Tools Used

• Python
• Flask
• Scikit-Learn
• Elastic Beanstalk

Acknowledgments

• Thank you to all the teachers for your unwavering support and courage to students everywhere