Interpreting Computing Pathways

Modeling Student Trajectories and Opportunity

This project examines how students navigate pathways into computing fields, combining computing education research with human-centered design, data science, and machine learning. Our goal is to make student pathways more visible, interpretable, and actionable for researchers, educators, and communities. Across all aspects of the project, we adopt a human-centered computing approach that prioritizes how student experiences are represented, interpreted, and designed for.

What This Project Does

This work brings together four core efforts: 

  • Studying student pathways in computing education, including progression, access, and structural barriers
  • Developing human-centered analytical approaches, including interpretable machine learning and statistical analysis
  • Building interactive tools and platforms to make findings accessible and usable
  • Collaborating with university and community partners to design and develop interventions that support access to and participation in computing education

Research Focus

Computing Education & Student Pathways

We investigate how students move through educational systems and into computing fields, identifying patterns in coursework, opportunity structures, and access to resources.

This work contributes new insights into how pathways are formed, where they break down, and how structural factors shape participation in computing.

Human-Centered Data Science & AI

We apply data science and machine learning methods — including clustering, natural language processing, and statistical analysis — to identify patterns in student data.

These approaches are designed to be interpretable, privacy-aware, and grounded in human-centered computing principles.

Tools & Interactive Platform

A central goal of this project is to develop an interactive hub for exploring student pathways in computing education.

We are building tools that:

  • Allow users to explore pathway patterns and outcomes
  • Translate complex analyses into accessible visual and narrative formats
  • Support research, decision-making, and public understanding

This platform will evolve into a standalone resource for researchers, educators, and communities. Details on the platform launch will be shared here by the Fall 2026 semester.

Contact The Team

The Interpreting Computing Pathways project is led by Dr. Takeria Braumuller, postdoctoral researcher in Georgia Tech's College of Computing. The work is supported by Dr. Tamara Pearson, Senior Director of Research & Programs for GT Constellations, and carried out in collaboration with a small research team. 

The project brings together expertise in computing education, data science, and human-centered design to study student pathways in computing and develop tools for broader research and community use.

For questions about this project, please contact Takeria Braumuller (tblunt3@gatech.edu).