Article: Development of an instructional model for Korean translation in multilingual classroom contexts Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.46 pp.114 - 140 Abstract: Multilingual classroom contexts pose significant pedagogical challenges, particularly when students have diverse native languages and varying levels of Korean proficiency. This study introduces a data-driven instructional model for Korean translation education that employs machine learning to address learner diversity. The model evaluates translation outputs, identifies learner-specific error patterns, and personalises instruction based on three key variables: native language influence, historical translation accuracy, and individual learning progression. A dataset of Korean translation tasks was collected from university students representing six L1 backgrounds - Chinese, Vietnamese, Arabic, Russian, Japanese, and Spanish. Texts were pre-processed through tokenisation, lemmatisation, and POS tagging, with Word2Vec embeddings used for feature extraction. The proposed Sparrow Search Optimiser Tuned Attention-based Sequence-to-Sequence (SSO-Attn-Seq2Seq) model demonstrated substantial improvements, achieving 88-91% across accuracy, precision, recall, and F1-score. Results highlight its adaptability in handling idiomatic expressions and syntactic variation, providing a scalable solution for multilingual Korean language education. Inderscience Publishers - linking academia, business and industry through research

Title: Development of an instructional model for Korean translation in multilingual classroom contexts

Authors: Rong Rong; Xiaojian Liu

Addresses: School of Foreign Languages, Liaodong University, Dandong, Liaoning, 118001, China ' School of Information Engineering, Liaodong University, Dandong, Liaoning, 118001, China

Abstract: Multilingual classroom contexts pose significant pedagogical challenges, particularly when students have diverse native languages and varying levels of Korean proficiency. This study introduces a data-driven instructional model for Korean translation education that employs machine learning to address learner diversity. The model evaluates translation outputs, identifies learner-specific error patterns, and personalises instruction based on three key variables: native language influence, historical translation accuracy, and individual learning progression. A dataset of Korean translation tasks was collected from university students representing six L1 backgrounds - Chinese, Vietnamese, Arabic, Russian, Japanese, and Spanish. Texts were pre-processed through tokenisation, lemmatisation, and POS tagging, with Word2Vec embeddings used for feature extraction. The proposed Sparrow Search Optimiser Tuned Attention-based Sequence-to-Sequence (SSO-Attn-Seq2Seq) model demonstrated substantial improvements, achieving 88-91% across accuracy, precision, recall, and F1-score. Results highlight its adaptability in handling idiomatic expressions and syntactic variation, providing a scalable solution for multilingual Korean language education.

Keywords: multilingual classroom settings; grammatical variations; languages; SSO-Attn-Seq2Seq.

DOI: 10.1504/IJICT.2025.150595

International Journal of Information and Communication Technology, 2025 Vol.26 No.46, pp.114 - 140

Received: 05 Sep 2025
Accepted: 26 Sep 2025
Published online: 17 Dec 2025 *

Title: Development of an instructional model for Korean translation in multilingual classroom contexts

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