Research Project

EAGER: Natural Language Processing for Teaching and Research in Engineering Education (NLPTREE)

Developing NLP pipelines for engineering education research

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National Science Foundation EAGER$299,6472022-2025Active StudyOpen for Students

Research Question

How can NLP methods help engineering education researchers and instructors analyze text-rich learning data responsibly and at scale?

Approach

  • Develop NLP workflows for student writing, discourse, and educational text analysis
  • Evaluate automated and AI-assisted analysis against human interpretation
  • Translate reusable methods into research and teaching artifacts

Evidence and Outputs

GrantAvailable

NSF EAGER award

Exploratory NSF project supporting natural language processing tools for engineering education research and teaching.

PublicationPublishedInternational Journal of Qualitative Methods2025

Leveraging Generative Text Models and Natural Language Processing to Perform Traditional Thematic Data Analysis

Peer-reviewed methodological paper on using generative text models and NLP for thematic analysis in education research.

People

  • PI: Dr. Andrew Katz
  • Research Scientist: Dr. Gabriella Coloyan Fleming
  • Undergraduate Research Assistants: Paul Oh