Classifying Students' Meta-cognitive Comments.

We report progress on automatically classifying written comments that students provide after receiving their performance on tests. The aim of this classification is to help teachers support the development of students' metacognitive skills more effectively. We describe a classification pipeline that seamlessly integrates large or small language models (LLMs or SLMs), leveraging state-of-the-art retrieval augmented generation, and human feedback. We apply our approach to field data from high school physics tests and to a classification scheme derived from a model for self-regulated learning. The best classification accuracies achieved for SLMs are of the order of 0.8, which is comparable to what can be obtained with LLMs. The classification obtained indicates that students in similar classroom contexts have very different perceptions and levels of analysis of their performance on assessments. While some focus solely on the factual interpretation of their quantitative results, others comment on their level of confidence, self-efficacy and learning strategies.