Productive struggle is a critical component of mathematics education, requiring students to actively work through ideas rather than just making errors. However, identifying this struggle from text transcripts is challenging because students often mask confusion with epistemic hedging rather than direct statements. Zero-shot large language models exhibit a conservative bias, systematically under-detecting struggle in classroom discourse. We introduce a two-stage NLP pipeline comprising a lexical heuristic gate and an LLM subtype classifier. Our model achieves 90.0% binary accuracy and 84.0% 4-category accuracy. We demonstrate the pedagogical value of this tool by showing that struggle is uniquely concentrated during explicit mathematical reasoning, offering educators a scalable method for root-cause analysis.