educational-data-mining
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- PaperarXiv — Language & NLP (cs.CL)13 Jul 2026
A Durability and Cross-Language Transfer Benchmark for a Validated Teaching-Feedback Classification Protocol
Esteban U. Vega Barajas
The study tested a previously validated teaching-feedback classification protocol on Spanish and English datasets using three generations of language representations. The protocol showed durability, with a 2026 frontier model achieving the highest thematic F1 on Spanish but no sentiment advantage over simpler models. Model choice was found to be a deployment decision rather than a property of the method.
Original abstract
Institutions collect far more open-ended teaching-evaluation feedback than they read. A prior study introduced a validated protocol for classifying such comments by thematic category and sentiment, built from a documented annotation guide, an intra-annotator reliability measurement, stratified cross-validation, and a held-out evaluation on a Spanish institutional corpus with a frozen-encoder design. Two questions limit its reuse: whether a protocol fixed to 2019-era frozen embeddings stays competitive as representation methods advance, and whether it transfers to a second language. We re-run it on the original Spanish data across three representation generations, sparse lexical features, frozen transformer embeddings, and prompted large language models, and transfer its sentiment task to English with a balanced 45,000-comment corpus checked against an aspect-labeled education dataset. Treating paired comparisons as descriptive, we find the protocol durable: a 2026 frontier model posts the highest thematic F1 on the hardest Spanish task, yet shows no sentiment advantage over a cheap model and no descriptive separation from it on English, so model choice is a deployment decision, not a property of the method.