Language models in automated essay scoring: Insights for the Turkish language

Yıl 2023, , 149 - 163, 27.12.2023

Tahereh Firoozi , Okan Bulut , Mark Gierl

https://doi.org/10.21449/ijate.1394194

Öz

The proliferation of large language models represents a paradigm shift in the landscape of automated essay scoring (AES) systems, fundamentally elevating their accuracy and efficacy. This study presents an extensive examination of large language models, with a particular emphasis on the transformative influence of transformer-based models, such as BERT, mBERT, LaBSE, and GPT, in augmenting the accuracy of multilingual AES systems. The exploration of these advancements within the context of the Turkish language serves as a compelling illustration of the potential for harnessing large language models to elevate AES performance in in low-resource linguistic environments. Our study provides valuable insights for the ongoing discourse on the intersection of artificial intelligence and educational assessment.

Anahtar Kelimeler

Automated essay scoring, Word embedding, Transformers, BERT, Turkish AES

Kaynakça

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