Interpretability built into a model's design (through domain-aligned inductive biases) generalizes better than post-hoc explanations, making it more useful for education researchers who need to find and analyze student reasoning in transcripts.
Researchers built an interpretable machine learning model that automatically detects when students in team conversations are engaging in mechanistic reasoning—understanding how things work. The model analyzes student utterances and group dynamics to assign probabilities of reasoning moments, using intentional design choices that improve accuracy on new students and contexts.