We study how people (in engineering) reason, learn, and decide.
A research lab in Engineering Education at Virginia Tech, founded in 2019 and now in its eighth year. Our work spans ten areas, from engineering ethics to public perceptions of AI. The map below holds every paper, what we found, and the tools built on it.
The research terrain71 papers, 2015 to 2026
Latest finding
An open-model workflow for thematic analysis found nearly all the themes planted in synthetic test data
Thematic analysis with open-source generative AI and machine learning: A new method for inductive qualitative codebook development (2026)
Each dot is a paper. An open embedding model, run on the lab's own machine, places papers about similar things near each other; the research areas act as a light guide. Contours rise where the work concentrates. Select a paper for details and its nearest neighbours, or an area to list its papers. Squares mark tools built on the research. A preprint and its published version count as one paper.
- Paper
- Paper with a published finding
- Tool built on it
Every paper on this map, by area
The field and its institutions
- Curriculum comparison: chemical and mechanical engineering education in the United States and Turkey (2025)
- Advanced Considerations in Quantitative Methods for New Directions in Engineering Education Research (2023)
- Analysis of Advances in Engineering Education Publications (2007-2020) to Examine Impact and Coverage of Topics (2022)
- Applying concepts from political science and economics to advance the study of engineering education (2022)
- Factors associated with collaboration networks in ASEE conference papers (2021)
- Taking stock: An Analysis of IJEE publications from 1996--2020 to examine impact and coverage of topics (2021)
- Learning from failures: Engineering education in an age of academic capitalism (2018)
- The revealing effects of disaster: A case study from Tulane University (2016)
Ethics, equity, and responsibility
- How do ethics and diversity, equity, and inclusion relate in engineering? A systematic review (2024)
- Collateral Damage: Investigating the Impacts of COVID on STEM Professionals with Caregiving Responsibilities (2022)
- Using Natural Language Processing to Explore Undergraduate Students’ Perspectives of Social Class, Gender, and Race (2022)
- Engineering ethics in engineering design courses: A preliminary investigation (2021)
- The correlation between undergraduate student diversity and the representation of women of color faculty in engineering (2021)
- Your views can be my views: Understanding differences in paradigms held by traditionally marginalized students in engineering (2021)
- An Investigation of When and Where Ethics Appears in Undergraduate Engineering Curricula (2020)
- Overcoming Challenges to Enhance a First Year Engineering Ethics Curriculum (2020)
- Monetizing Life May Be the Ethical Thing to Do (2019)
- Investigating influences on first-year engineering students’ views of ethics and social responsibility (2018)
- Factors related to faculty views of undergraduate engineering ethics education (2017)
- Telling it like it was: Histories of change in engineering ethics education (2017)
- Shadow codes of engineering ethics: An experiment in ethics imaginaries (2016)
- Educating for a revolution: Discerning together the Highlander idea and its lessons for ESJP’s work (2015)
How faculty think, teach, and assess
- Understanding instructor decision-making in engineering education for sustainable development: a comparison of institutions in Denmark and the United States (2025)
- Paradigm Shift? Preliminary Findings of Engineering Faculty Members’ Mental Models of Assessment in the Era of Generative AI (2024)
- Exploring Faculty Members' Conceptualizations of Diversity, Equity, and Inclusion in Engineering Education (2023)
- Promoting Research Quality to Study Mental Models of Ethics and Diversity, Equity, and Inclusion (DEI) in Engineering (2023)
- WIP: Faculty Use of Metaphors When Discussing Assessment (2023)
- Defining Assessment: Foundation Knowledge Toward Exploring Engineering Faculty’s Assessment Mental Models (2022)
How students learn and experience engineering
- Exploring the Impact of Engineering Projects in Community Service on Engineering Students’ Perspectives about Engineering as a Major (2023)
- How Participating in Extracurricular Activities Supports Dimensions of Student Wellness (2023)
- Development of hybrid laboratory sessions during the COVID-19 Pandemic (2022)
- Students’ Feedback About Their Experiences in EPICS Using Natural Language Processing (2022)
- Understanding First-year Engineering Students’ Perceptions of Working with Real Stakeholders on a Design Project: A PBL Approach (2022)
- Using Sentiment Analysis to Evaluate First-year Engineering Students Teamwork Textual Feedback (2022)
- Harvesting tweets for a better understanding of engineering students' first-year experiences (2020)
- Using Chatbots as Smart Teaching Assistants for First-Year Engineering Students (2020)
Sustainability and climate
- Inspiring Sustainability in Undergraduate Engineering Programs (2024)
- A Thematic and Trend Analysis of Engineering Education for Sustainable Development (2022)
- Augmented Reality for Sustainable Collaborative Design (2022)
- Civil Engineering Students’ Beliefs about Global Warming and Misconceptions about Climate Science (2021)
- Higher perceived design thinking traits and active learning in design courses motivate engineering students to tackle energy sustainability in their careers (2021)
- Predicting engineering students’ desire to address climate change in their careers: An exploratory study using responses from a U.S. National survey (2021)
- Civil Engineering Students’ Beliefs about the Technical and Social Implications of Global Warming and When Global Warming Will Impact Them Personally and Others (2020)
Careers and the workforce
- Using generative AI for large-scale qualitative analysis of social media posts to understand why people leave computer science (2025), read the finding
- Engineering students' interests in nonprofit and public policy careers: Applying a data-driven approach to identifying contributing factors (2025), read the finding
- Skill Development of Engineering and Physical Science Doctoral Students: Understanding the Role of Advisor, Faculty, and Peer Interactions (2024)
- What engineering employers want: An analysis of technical and professional skills in engineering job advertisements (2024)
- An Empirical Study of Programming Languages Specified in Engineering Job Postings (2022)
Technology policy
Language technology for education research
- Extending Minimal Pairs with Ordinal Surprisal Curves and Entropy Across Applied Domains (2026)
- Automated Analysis of Knowledge Types in Computer Science Textbooks: A Natural Language Processing Approach to Understanding Epistemic Climate (2025)
- A Reinforcement Learning Framework for N-Ary Document-Level Relation Extraction (2024)
- Exploring NLP-based Methods for Generating Engineering Ethics Assessment Qualitative Codebooks (2023)
- Pushing Ethics Assessment Forward in Engineering: NLP-Assisted Qualitative Coding of Student Responses (2023)
- Utilizing Natural Language Processing to Examine Self-Reflections in Self-Regulated Learning (2023)
- Clustering-based unsupervised generative relation extraction (2022)
- Work-in-Progress: Using Latent Dirichlet Allocation to uncover themes in student comments from peer evaluations of teamwork (2022)
- Figurative language in computer education: Evidence from YouTube instructional videos (2021)
- Using natural language processing to facilitate student feedback analysis (2021)
- Reinforcement Learning-based N-ary Cross-Sentence Relation Extraction (2020)
Generative AI for qualitative research
- Thematic analysis with open-source generative AI and machine learning: A new method for inductive qualitative codebook development (2026), read the finding
- Advancing Qualitative Analysis in Professional Disaster and Risk Communication: A Comparative Study of an OpenAI ChatGPT 3.5 Model-Enabled Method for Processing Complex Public Posts (2025)
- Expanding possibilities for generative AI in qualitative analysis: Fostering student feedback literacy through the application of a feedback quality rubric (2025), read the finding
- Leveraging Generative Text Models and Natural Language Processing to Perform Traditional Thematic Data Analysis (2025)
- From Manual Coding to Machine Understanding: Students' Feedback Analysis (2024)
- Novel Approach Designing Interview Protocols with Generative Large Language Models to Study Mental Models and Engineering Design (2024)
- Stumbling Our Way Through Finding a Better Prompt: Using GPT-4 to Analyze Engineering Faculty’s Mental Models of Assessment (2024)
- Using Generative Text Models to Create Qualitative Codebooks for Student Evaluations of Teaching (2024)
- Advancing qualitative analysis: An exploration of the potential of generative AI and NLP in thematic coding (2023)
- Exploring the Efficacy of ChatGPT in Analyzing Student Teamwork Feedback with an Existing Taxonomy (2023)
- The Utility of Large Language Models and Generative AI for Education Research (2023)
Recent findings
Each one is a single result from a published paper, with its figure and its citation.
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A language model scoring student peer feedback struggled on the same rubric criteria that human raters found hardest
Applying a four-part feedback-quality rubric to 295 peer comments, an open-source language model agreed well with researchers on whether a comment named a problem or suggested a fix, and poorly on the two criteria where the researchers also agreed least.
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An open-model workflow for thematic analysis found nearly all the themes planted in synthetic test data
When the lab planted known themes in three synthetic datasets, the GATOS workflow produced a good match for 166 of 187 planted sub-themes, a partial match for 18 more, and missed 3.
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Every reason people gave for leaving computer science showed up at every stage, from degree program to industry job
In 263 Reddit posts about leaving computer science, all six reasons the study identified appeared at all four points of departure, from students leaving a degree program to professionals leaving computing jobs, though the mix shifted by stage.
Built on the research
When a method works, we build it into software. One is open to the public today; the rest are research prototypes we can demonstrate.
Work with us
Every path starts with an email.
- Academic collaborators
Joint studies, proposals, and shared methods across institutions and disciplines.
- Industry and organizations
Scoped analyses of text and interview data, reproducible workflows, workshops, and training.
- Students
Graduate study through Engineering Education at Virginia Tech, and undergraduate research roles each semester.
- Talks, media, and the public
Speaking requests, media inquiries, and plain-language findings anyone can follow.
People
Faculty, research scientists, and PhD students, working alongside 5 undergraduate researchers.
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Associate Professor
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Research Scientist
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PhD student
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PhD student
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PhD student
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PhD student