Classifying Students’ Meta-cognitive Comments

Abstract

International audienceWe report progress on automatically classifying written commentsthat students provide after receiving their performance on tests.The aim of this classification is to help teachers support the developmentof students’ metacognitive skills more effectively. We describe a classificationpipeline 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 highschool physics tests and to a classification scheme derived from a modelfor self-regulated learning. The best classification accuracies achieved forSLMs are of the order of 0.8, which is comparable to what can be obtainedwith LLMs. The classification obtained indicates that students insimilar classroom contexts have very different perceptions and levels ofanalysis of their performance on assessments. While some focus solely onthe factual interpretation of their quantitative results, others commenton their level of confidence, self-efficacy and learning strategies