Clustering student argumentation types by implementing a multi-document clustering model: a combination of Pytorch and BERT

Automated evaluation of argumentative writing has emerged as an essential field in educational technology, providing systematic feedback on student essays to improve critical thinking and writing quality. This study examines two complementary approaches: the PERSUADE model (Personalized Evaluations and Recommendations for Students Using Argumentation Data and Evidence), which relies on annotated discourse elements, and a multi-document clustering (MDC) model implemented with PyTorch and transformer embeddings. The MDC model, when implemented with Bidirectional Encoder Representations from Transformers (BERT)-base, achieved 78 percent accuracy and clustering metrics indicating strong separation (Silhouette Score 0.789, Davies–Bouldin Index 0.304, Calinski–Harabasz Score 3,529.2). To test the impact of richer embeddings, the MDC framework was extended with Robustly Optimized BERT Pretraining Approach (RoBERTa) and Decoding-enhanced BERT with disentangled attention (DeBERTa). The results show consistent performance improvements: RoBERTa reached 89 percent accuracy with higher clustering stability, while DeBERTa-v3-large achieved the strongest performance at 91 percent accuracy, with the best clustering metrics (Silhouette Score 0.817, Davies–Bouldin Index 0.276, Calinski–Harabasz Score 3,788.5). These findings confirm that the choice of encoder significantly influences clustering coherence and classification effectiveness. The comparative analysis highlights complementary strengths: PERSUADE excels in micro-level discourse evaluation, while MDC, enhanced by advanced embeddings, offers macro-level organization of argumentative structures. Together, these approaches demonstrate the potential for integrated frameworks that capture both discourse depth and cross-document clustering.