자료 기반 학습(Data-driven learning)을 통한 한국어 학습자의 쓰기 오류 자가 교정 연구
A Study of Self Correction for Writing Error by Learners of Korean Language based on Data-Driven Learning
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Abstract
This study aims to design and implement a data-driven learning (DDL) approach that enables Korean language learners to self-correct their writing errors, and to examine its effectiveness. For this purpose, seven learners participated in the study over the course of one semester. Each learner wrote four data commentaries and four argumentative essays. An instructional method was developed that allowed learners to consult a corpus to correct the errors generated during this writing process. As a result, all learners demonstrated a successful error correction rate of over 50%, with a significant decrease in writing errors afterwards. Additionally, students demonstrated fewer errors and a higher rate of successful correction in data commentaries than argumentative essays. Survey and interview results indicated that learners' sensitivity to errors and confidence in their ability to correct them increased after the course. However, further research is recommended regarding learners' satisfaction and preference for the DDL-based error correction method, as the effectiveness of DDL-based correction varies by the writing genre and the learners' preference. Therefore, it is necessary to tailor the size and scope of corpora to suit the characteristics of individual learners.