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 toward desired, domain-aligned behavior. Experiments compare trained models with and without the inductive bias components, investigating whether their presence improves the desired model behavior on transcripts involving never-before-seen students and a novel discussion context. Our results show that the inductive bias improves generalization -- supporting the claim that interpretability is built into the model for this task rather than imposed post hoc. We conclude with practical recommendations for STEM education researchers seeking to adopt the tool and for ML researchers aiming to extend the model's design. Overall, we hope this work encourages the development of mechanistically interpretable models that are understandable and controllable for both end users and model designers in STEM education research.

翻译：STEM教育研究者通常关注识别学生机械推理的瞬间以进行深度分析，但受限于人工审查大量团队对话记录的能力，难以定位具有高浓度此类推理的片段。我们提出一种可解释机器学习模型作为解决方案，该模型输出个体学生参与机械推理行为的时变概率，利用学生自身话语及团队其他成员的贡献作为证据。通过刻意设计的概率模型工具包，我们引入特定归纳偏置，引导概率动态向期望的领域对齐行为发展。实验比较了有无归纳偏置组件的训练模型，考察其对包含未见学生和新颖讨论背景的对话记录中模型期望行为的影响。结果表明，归纳偏置提升了泛化能力——支持该任务中可解释性内置于模型而非事后强加的观点。我们最终为STEM教育研究者采用该工具及机器学习研究者扩展模型设计提供了实用建议。总体而言，希望本研究能推动STEM教育研究中兼顾终端用户与模型设计者理解与可控的机械可解释模型的发展。