Locating acts of mechanistic reasoning in student team conversations with mechanistic machine learning
STEM education researchers are often interested in identifying moments of students' mechanistic reasoning for deeper analysis, but have limited capacity to search through many team conversation transcripts to find segments with a high concentration of such reasoning. We offer a solution in the form of an interpretable machine learning model that outputs time-varying probabilities that individual students are engaging in acts of mechanistic reasoning, leveraging evidence from their own utterances as well as contributions from the rest of the group. Using the toolkit of intentionally-designed probabilistic models, we introduce a specific inductive bias that steers the probabilistic dynamics...
| Authors | Kaitlin Gili, Mainak Nistala, Kristen Wendell et al. |
|---|---|
| Primary category | physics.ed-ph |
| Published | 23 April 2026 |
| Last updated at source | 23 April 2026 |
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