Across the mind sciences, first-person reports are already woven into everyday practice—from button-press self-ratings to open interviews—yet they rarely receive sustained methodological attention. This talk asks how we can elicit and integrate them better. I introduce micro-phenomenology as a disciplined way to obtain fine-grained descriptions of lived experience and exemplify it with pilot studies on mathematical cognition—especially problem solving and proof comprehension. I argue that structured first-person inquiry can render learning theories more meaningful for learners and illuminate tacit dimensions of (mathematical) expertise. Brief vignettes illustrate how these descriptions can be aligned with co-registered gaze and interaction traces to support analysis and validation. While the cases come from mathematics education, the methodological issues—how to elicit, analyze, and integrate first-person data—are relevant across research on mind, learning, and behavior.
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Qualitative & quantitative data collection methods
