Student opinion mining: Automated topic extraction from student feedback
  • Metaadatok
Tartalom: http://publikacio.uni-eszterhazy.hu/9293/
Archívum: Eszterházy Károly Katolikus Egyetem Publikáció
Gyűjtemény: Típus = Folyóiratcikk - Journal article
Cím:
Student opinion mining: Automated topic extraction from student feedback
Létrehozó:
Czimbalmos, Olivér
Szántó, Zsolt
Kőrösi, Gábor
Becsei, Péter
Udvari, Beáta
Farkas, Richárd
Tartalmi leírás:
While Student Evaluation of Teaching Work (SETW) is a cornerstone of quality assurance in higher education, the high volume and linguistic complexity of unstructured qualitative feedback often lead to its underutilization in institutional decision-making. This study addresses this “analysis gap” by developing an automated pipeline to process 34,000 unique Hungarian student responses from a major research university. To empower academic administration and faculty leadership with the ability to uncover latent thematic patterns within these responses, we propose a hybrid NLP framework that utilizes a Large Language Model (LLM) for thematic reclassification and Aspect-Based Sentiment Analysis (ABSA), combined with an unsupervised layer for fine-grained latent topic discovery using transformer-based embeddings and HDBSCAN clustering. Our proposed pipeline successfully identified new granular latent topics – such as lecture pacing, traceability, material accessibility and slides – providing a level of diagnostic detail that remains invisible to standard quantitative metrics. The results prove that modern natural language processing (NLP) techniques can effectively transform raw, unstructured student narratives into objective, actionable diagnostic tools.
Nyelv:
angol
angol
Típus:
Folyóiratcikk - Journal article
NonPeerReviewed
Formátum:
text
Azonosító:
Czimbalmos, Olivér, Szántó, Zsolt, Kőrösi, Gábor, Becsei, Péter, Udvari, Beáta, Farkas, Richárd (2026) Student opinion mining: Automated topic extraction from student feedback Annales Mathematicae et Informaticae. 63. pp. 42-54.
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