Brendan Eagan
University of Wisconsin-Madison, WI·Education Research
Classrooms, labs and online learning tools now generate huge amounts of text and talk. Examples include classroom conversations, team problem-solving, software log files and chats with AI tutors.
- Fields
Education Research
Social Science
Computer Science
- Topics
quantitative ethnography
STEM education research
learning analytics
- Grants
- 2 known grants, through Sep 2028
What they work on
- Train three cohorts of 30 mostly underrepresented early-career researchers in quantitative ethnography (QE) through the Accelerator, Incubator, Booster and Multiplier workshop sequence covering methods, feedback, writing and teaching.
- Support researchers in building models of large-scale qualitative learning data that are fair and actionable for diverse learners and also statistically valid and generalizable.
- Run a year-long QE training of pilot analyses, a weeklong intensive and individual consultations, preparing researchers to analyze classroom talk, learning-software logs and AI tutor transcripts.
- Prepare Institute graduates to lead interdisciplinary QE teams, mentor junior researchers, and run QE workshops at their home institutions and at conferences.
More about the research
Researchers want to turn these records into models of how students learn, without losing the meaning that qualitative, interpretive analysis captures. Quantitative ethnography (QE) offers one route. It is a set of statistical, computational and AI-powered techniques that combine qualitative interpretation with quantitative modeling. The resulting models aim to be both faithful to learners' experiences and statistically valid. The two projects run the Quantitative Ethnography Institute, which trains STEM education researchers to use QE.
Why it matters
Educators deciding on curricula, teaching approaches and personalized instruction need models of learning that make sense of large conversational datasets. These include data from AI-based learning platforms. Training a larger community of researchers in quantitative ethnography could lead to learning analytics that are statistically sound and fair to diverse learners. Such tools could inform decisions by teachers and education policy leaders.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Tracy Sweet
University of Maryland, MD·Education Research
Train three cohorts of 17 early career STEM education scholars through year-long intensive institutes on research design, measurement and data analysis for large-scale studies in K-12 and postsecondary settings.
Sijia Zhang
University of North Carolina at Charlotte, NC·Computer Science
Develop teaching materials on planning, configuring and using cloud computing resources and frameworks such as Hadoop and federated learning for educational analytics tasks.
Azadeh Bolhari
University of Colorado Boulder, CO·Education Research
Train about 48 STEM faculty and postdocs to approach education research as a design problem, using the ProQual Institute's 4-step program in interpretive research design.
The school
University of Wisconsin-Madison: 91 labs on PI Pages, $1.9B a year on research, 854 research doctorates a year.
Reach out
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