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p-Index From 2021 - 2026
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All Journal EDULITE: Journal of English Education, Literature and Culture Journal of English Language Studies
K.J. Vargheese
Christ College (Autonomous) Irinjalakuda, Kerala, India
Education Languange, Linguistic, Communication & Media

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Teacher-Student Style Mismatch: Implications for English Language Learning and Classroom Dynamics Andi Asrifan; Luís Miguel Oliveira de Barros Cardoso; K.J. Vargheese
Journal of English Language Studies Vol 10, No 2 (2025): Available Online in September 2025
Publisher : English Department - University of Sultan Ageng Tirtayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62870/jels.v10i2.31747

Abstract

Comprehending discrepancies between instructor and student learning styles is crucial for enhancing English language acquisition and classroom interactions. This study specifically sought to (1) ascertain the degree of discrepancies between teachers’ instructional preferences and students’ learning styles, (2) investigate the impact of these discrepancies on student engagement, motivation, and classroom interaction, and (3) analyze the adaptive strategies utilized by both educators and learners. A convergent mixed-methods methodology was utilized, integrating surveys (VARK and Grasha inventory), classroom observations, and interviews with ten educators and one hundred students. The findings indicated a notable discrepancy: 70% of educators supported auditory-based education, whereas merely 30% of students preferred this approach, resulting in diminished engagement, frustration, and disengagement among visual and kinesthetic learners. Nonetheless, certain students formulated adaptive tactics, including the creation of visual aids and collaboration with others. These findings underscore the necessity for multimodal instructional strategies and specialized teacher training to cultivate inclusive educational settings.
Automated feedback for speaking and writing skills: Deep learning in English language assessment Andi Asrifan; Luís Miguel Oliveira de Barros Cardoso; K.J. Vargheese
EduLite: Journal of English Education, Literature and Culture Vol 11, No 1 (2026): February 2026
Publisher : Universitas Islam Sultan Agung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30659/e.11.1.67-85

Abstract

The incorporation of Artificial Intelligence (AI) into language evaluation has revolutionized how learners receive feedback on their speaking and writing abilities. Nevertheless, empirical information about the precision and educational efficacy of AI-generated feedback—especially in advanced language competencies—continues to be scarce. This study seeks to evaluate the efficacy of deep learning–driven automated feedback systems in enhancing English learners' speaking and writing skills. The study utilized a mixed-methods research approach and included 100 undergraduate students participating in an English for Academic Purposes course, focusing on English as a Foreign Language (EFL). Quantitative data were gathered via pre-test and post-test writing and speaking activities evaluated using AI tools (Grammarly, ETS e-rater, and Google Automatic Speech Recognition), whilst qualitative data were derived from surveys and interviews to capture learners' impressions. The findings demonstrate statistically significant enhancements in grammatical accuracy, lexical diversity, coherence, fluency, pronunciation, and intelligibility following exposure to AI-generated feedback. However, inconsistencies were identified between AI and human assessments regarding speech coherence and contextual relevance. The results indicate that AI-generated feedback serves as an excellent additional evaluation instrument, especially for form-focused linguistic elements, however it is constrained in its ability to measure higher-order communication competencies. This study underscores the significance of amalgamating AI-driven feedback with human discernment to establish a more holistic and pedagogically robust language assessment framework.
Co-Authors Asrifan, Andi Luís Miguel Oliveira de Barros Cardoso