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All Journal International Journal of Evaluation and Research in Education (IJERE)
Siripol, Piyathat
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Education

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Classroom language training for non-English pre-service teachers: a professional development project Siripol, Piyathat; Wilang, Jeffrey D.
International Journal of Evaluation and Research in Education (IJERE) Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v13i5.28642

Abstract

As the English language is required to be used beyond English subject classrooms in English as a foreign language (EFL) country, teachers from various disciplines, such as math and science, may find it challenging when having to conduct the course in English. Therefore, this professional development project aimed to address the issue of math and science pre-service teachers lacking classroom language knowledge when teaching in an EFL setting. The project involved a month-long training program for pre-service teachers on how to use English classroom language in teaching Math and Science to elementary and high school students. Pre- and post-training video recordings were collected to evaluate the linguistic development of the participants in classroom language use. Additionally, journal entries were collected to know the participants' insights on pedagogical growth and perceptions when using English in teaching content lessons. The findings indicated a significant improvement in the participants' classroom language use in various areas such as greetings and lesson introductions, feedback and instructions, classroom management, requests and questions, and lesson conclusions. The pre-service teachers also reflected on their pedagogical development in their journals. The article discusses some implications of the project that could benefit similar initiatives in EFL settings that use English as partial or full medium of instruction.
Evaluating the consistency of automated CEFR analyzers: a study of English language text classification Siripol, Piyathat; Rhee, Seongha; Thirakunkovit, Suthathip; Liang-Itsara, Aphiwit
International Journal of Evaluation and Research in Education (IJERE) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v14i4.33528

Abstract

With the increasing use of web-based tools for text analysis, there is a growing reliance on automated systems to assess text difficulty and classify texts to the Common European Framework of Reference for Languages (CEFR). However, inconsistencies in these tools’ outputs could undermine their effectiveness for language learners and researchers. This study investigates the consistency of five widely used automated CEFR analyzer tools, including ChatGPT, by analyzing 20 English descriptive texts at CEFR levels B1 and B2. A quantitative approach was employed to compare the CEFR classifications generated by these tools. The results reveal significant inconsistencies across the tools, raising concerns about the reliability of automated CEFR alignment. Additionally, the content and genre of texts appeared to influence the CEFR classification, suggesting that certain factors beyond the tools’ algorithms may affect their accuracy. These findings have important implications for language educators, curriculum designers, and researchers who rely on automated CEFR tools for text selection, grading, and analysis. The study highlights the limitations of automated CEFR classification systems and calls for a more qualitative approach to text difficulty alignment analysis. Future research recommendation is discussed and call for more focus on refining these tools and exploring additional factors that may impact their effectiveness in text difficulty measurement and CEFR alignment.
Co-Authors Liang-Itsara, Aphiwit Rhee, Seongha Thirakunkovit, Suthathip Wilang, Jeffrey D.