Chongchit, Yenying
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Preferred name
Chongchit, Yenying
Main Affiliation
Email
yenying.ch@kmitl.ac.th
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Item type:Publication, Integrating Corpus-based Methods to Determine Grammatical Topics for Teaching English Writing in the Thai Context(2025-06-01); Grammatical knowledge for effective English writing remains a significant need among Thai university students, while identifying the relevant grammatical topics to address this need continues to be a challenge in teaching writing courses. Previous papers on writing in Thai universities have mainly focused on writing performance issues, rather than proposing methods to address these issues, particularly in grammar. This quantitative case study employed corpus analyses to determine the relevant grammatical topics that address the grammatical needs of a specific group of Thai university students in English writing. ChatGPT was utilized to improve the essays of the target students, resulting in two datasets (namely, students’ essays and refined essays). Keyword analysis was employed to illustrate their grammatical characteristics. Biber’s (1988; 1989) multidimensional analysis and key part-of-speech analysis were applied to verify the keyword findings and determine the target grammatical topics for teaching writing. The analyses revealed 12 significant grammatical features that address the grammatical needs of the target students in English writing (namely, nominalizations, determiners, present participial clauses, prepositional phrases, attributive adjectives, conjuncts, independent clause coordination, phrasal coordination, past participial clauses, that relative clauses in the subject position, adverbial subordinators, and sentence relatives). These features can make the students’ writing compositions informational, narrative, and formal, reflecting the characteristics of academic language. This paper provides procedures for identifying grammatical topics that are aligned with the current grammatical needs of students in writing. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Semantic Tagging of AI-Generated Science Texts to Identify Topics for Teaching STEM in K-12 Contexts(2025-01-01); ; As STEM education continues to play a crucial role in preparing students for future challenges, finding effective ways to improve its content and instructional delivery, especially in K-12 settings, requires ongoing attention and innovation. This study employed semantic tagging on AI-generated science texts to identify key semantic tags that can serve as topics for STEM instruction. Two datasets were analyzed using the English USAS semantic tagger and AntConc tool. The findings highlight topic-specific areas associated with animal and vegetable production (e.g., domestic animals, health and disease, farming and horticulture), increasing the students' perspectives on agricultural practices, technological applications, system designs, and mathematical knowledge. The semantic tags identified can inform and enrich STEM curriculum design, offering educators a data-driven approach to selecting relevant and appropriate content for K-12 instruction. Overall, this study provides steps to examine other STEM-related texts for the development of curriculum design from a linguistic approach.