Retrieval-Conditioned Counterfactual Consistency for Classroom Discourse Analysis

Classroom discourse analysis depends on detecting pedagogically meaningful reasoning from turns whose relevant context may occur far earlier. This study evaluates a small-footprint retrieval-conditioned classifier augmented with an explicit counterfactual consistency penalty. We manually curated 136 publicly displayed, human-labeled student-response records from the TalkMoves corpus (47 labeled as providing evidence or reasoning). A controlled benchmark inserts each record’s original paired context among 8, 32, or 64 distractor turns sampled from the same public excerpt pool; these reconstructed histories are synthetic, whereas the classification labels are authentic. A character n-gram TF-IDF selector supplies one context to a word n-gram logistic classifier. We adapt the symmetric Kullback–Leibler consistency objective and Counterfactual Flipping Rate audit of the neuro-symbolic pedagogical alignment literature to a binary, CPU-feasible estimator. Across five grouped random splits, the 32-candidate setting attains Macro-F1 0.753 with consistency regularization and 0.753 without it, while mean flipping falls from 10.38% to 3.07%. Retrieval recall at one declines as context grows, and the utterance-only baseline remains competitive. Thus, the supported contribution is improved stability under specified orthographic perturbations, together with a reproducible measurement of retrieval failures—not a claim of superior full-lesson performance or demographic fairness.