Redesigning a Machine Learning Platform for K-12 Students

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Redesigning a Machine Learning Platform for K-12 Students

Making machine learning intuitive for K-12 classrooms.

ROLE

Lead Product Designer

PROBLEM

A new machine learning platform designed for K-12 classrooms was creating significant friction for its users due to core usability issues. Students and teachers were encountering recurring obstacles, hindering the learning process and leading to low task completion rates, which created a significant barrier to the platform's educational goals.

RESULTS

Increased task completion rates by 15%

Redesigned core components to solve recurring usability issues

Identified key usability improvements to enhance the K-12 user flow

ROLE

Lead Product Designer

PROBLEM

RESULTS

Increased task completion rates by 15%

Redesigned core components to solve recurring usability issues

Identified key usability improvements to enhance the K-12 user flow

Project Results

CODAP Plugin for students to use Machine Learning components in their datasets

Heuristic Evaluation

Looked at current platform to find user pain points and how to address them in CODAP Plugin.

Why CODAP?

Low Fidelity Prototyping

High Fidelity Prototyping

Component Library

Design Changes

Demo of CODAP Plugin

Try it out!

Final Outcome

The platform's recurring usability flaws were creating a frustrating experience, so I redesigned core Figma components to directly address these problems. This component-level fix enhanced the overall user flow and led to a measurable 1R% increase in task completion rates. By identifying and solving these usability gaps, we made the complex machine learning platform more intuitive and effective for its new K-12 audience.