Holly Taylor
Tufts University, MA·Psychology
Ambiguity, uncertainty and confusion run through everyday life. They come up when students work together on a physics problem, when EMTs (emergency medical technicians) respond to an accident, and when a doctor makes a diagnosis from vaguely described symptoms.
- Fields
Psychology
Education Research
Computer Science
- Topics
uncertainty
ambiguity
confusion
- Grants
- 1 known grant, through Aug 2029
What they work on
- Examine how individuals and small groups engage with ambiguity, uncertainty and confusion while solving STEM problems in controlled lab studies.
- Follow a diverse group of STEM students through their first two years of classes, collecting behavioral, language and physiological data to track uncertainty over long time scales.
- Develop wearable sensors that unobtrusively record physiological signals for long periods, showing how uncertainty affects the body's state during learning.
- Build machine learning, natural language processing and dynamical models that combine multimodal data to model uncertainty in individuals, small groups and classrooms.
More about the research
Yet there is no systematic understanding of how people handle these moments, alone or in groups. Schools usually treat confusion as something to avoid, which can cause stress and leave students stuck. Classroom and lab research suggests a different approach. When uncertainty is presented as an exciting challenge, it can motivate students and improve how much they learn.
Why it matters
Students and trainees often stall when they get confused. This work could show teachers and trainers how to help learners treat uncertainty as a productive challenge rather than a source of stress. The same insight may carry over to professionals who must act under uncertainty, such as emergency responders and physicians making diagnoses. The new sensing and modeling methods could also let researchers study learning in real classrooms as it happens.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Anthony Sicilia
West Virginia University, WV·Computer Science
Develop a Probably Approximately Correct (PAC) learning theory that measures how user disagreement, suggestions and topic shifts change the calibration of an AI system's confidence during a conversation.
Tahir Ekin
Texas State University, TX·Computer Science
Develop an Applied AI Summer School and new course modules in data analytics, Python, deep learning, natural language processing and computer vision for students in non-AI disciplines, using inclusive and contextualized teaching methods.
Milos Gligoric
University of Texas at Austin, TX·Computer Science
Develop a language and framework for writing fine-grained tests that check individual statements and code fragments inside functions, and evaluate it on open-source projects.
The school
Tufts University: 19 labs on PI Pages, $278M a year on research, 144 research doctorates a year.
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