ScholarWorks@경상국립대학교

초록

This study implemented a prediction model that can predict the age group of elementary and secondary learners’ writings using the Transformer language model, especially KoBERT, KcBERT, and KoBART. By conducting fine-tuning for the "National Language Institute's non-published corpus (version 1.1)" among the "everyone corpus" based on the KoBERT, KcBERT, and KoBART which are pre-trained for large-scale corpus, an age prediction model was embodied. As a result, the prediction model was found to have an accuracy of about 61.1% to 68.1%, and among them, the prediction model based on KoBART showed the best performance. In particular, when fine-tuning was conducted in consideration of the genre of writings, the accuracy was up to 70% or more, confirming the possibility of elaborating the age prediction model for elementary and secondary learners’ writings. In addition, this paper extracted learners’ writings that are judged to be typical for each age group by using the age prediction model. Through this, it is possible to analyze the development aspect of learners’ writings, and furthermore, it is expected to lay the foundation for searching for clues to evaluations for learners’ writings or teaching & learning content of writing based on typical and representative learners’ writings.

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