As student cohorts grow, real-time case-based learning discussions generate increasing volumes of textual data, intensifying the orchestration load teachers must manage. Reviewing and providing feedback on student responses promptly becomes increasingly challenging, demanding efficient methods to assist educators in selecting relevant contributions to steer classroom discussions. This study proposes a low-footprint natural language processing (NLP) approach that leverages small-scale models running on commodity hardware, avoiding the computational overhead and cost associated with large language models. Our system, integrated into EthicApp, a collaborative learning platform, employs pre-trained language models such as the Universal Sentence Encoder (USE) and Bidirectional Encoder Representations from Transformers for Spanish (BETO), along with traditional text-mining techniques like Term Frequency-Inverse Document Frequency (TF-IDF). Through expert evaluations, we found that BETO exhibited superior performance in identifying relevant student responses but required GPU acceleration. At the same time, USE provided an efficient alternative that outperformed TF-IDF and remained feasible for CPU-based execution. Additionally, the methods showed a tendency—most notably BETO—to select longer responses, which, rather than introducing selection bias, was interpreted as an indicator of deeper student engagement. No significant semantic bias was found, ensuring a fair representation of students’ perspectives. Our findings suggest that low-footprint NLP can effectively reduce teacher orchestration load, enabling more targeted feedback without requiring extensive computational resources. 

Natural Language Processing, Collaborative and social computing systems and tools, Laboratory experiments