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

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

National Science Foundation EAGER, $299,647, 2022-2025. Completed.

Developing NLP pipelines for engineering education research

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

Objectives

Create specialized NLP tools for engineering education
Analyze student writing and discourse patterns
Build automated feedback systems

Evidence and materials

NSF EAGER award: Exploratory NSF project supporting natural language processing tools for engineering education research and teaching. Status: Available.
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. Status: Published.

Tools from this project

Self-Regulated Teaching Intelligence

Team

PI

Andrew Katz

Research Scientist

Gabriella Coloyan Fleming

Undergraduate Research Assistants

Paul Oh