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Muhammad Nafi Annury, Muhammad
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Enhancing Writing Skills through Cultural Integration: Exploring the Impact of Culturally-Responsive Writing Instruction in Diverse EFL Classrooms Sutrisno, Djoko; Abbas, Azwar; Nafi Annury, Muhammad
Global Synthesis in Education Journal Vol. 2 No. 3 (2024): Vol. 2 No. 1 (2024): Vol. 2 No. 2 (2024): November 2024, Pages 01-77
Publisher : Mutiara Intelektual Indonesia Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61667/rpm4cp30

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In an increasingly multicultural and interconnected world, the integration of culturally-responsive teaching methods into English as a Foreign Language (EFL) instruction has emerged as a vital pedagogical approach. This study examines the impact of culturally-responsive writing instruction on the development of EFL learners' writing skills, comparing it with traditional writing instruction. The objective is to explore how integrating cultural elements into writing pedagogy enhances linguistic competencies, fosters engagement, and promotes inclusivity in diverse classrooms. A mixed-methods research design was employed, combining a quantitative pre-test and post-test experimental approach with qualitative interviews and focus group discussions. The quantitative phase of the study involved assessing writing performance in two groups: one experimental group that received culturally-responsive instruction and one control group that followed traditional teaching methods. Writing samples were evaluated using standardized rubrics to measure key competencies such as content, organization, grammar, and critical thinking. The qualitative phase gathered insights into students' perceptions, motivation, and engagement through thematic analysis of interviews and focus group discussions. The results demonstrated significant improvements in the experimental group's writing performance, with notable gains in grammar (+40.9%), critical thinking (+41.7%), and vocabulary (+35.3%). Students reported higher engagement, confidence, and personal connection to writing tasks. Teachers observed improved participation and deeper critical analysis in student writing. However, challenges such as balancing curriculum demands and limited institutional support were highlighted. Overall, the findings underscore the transformative potential of culturally-responsive instruction in improving writing outcomes, fostering cultural appreciation, and creating inclusive learning environments. This study contributes to the growing body of evidence advocating for educational equity and culturally-responsive practices in EFL classrooms.
Reviving the Classical Paradigm: Exploring the Timeless Principles of Classical Education in the Modern World Nananukul, Naramethi; Nafi Annury, Muhammad; Minh Hoangh, Trinh; Soontarin, Nupap
Global Synthesis in Education Journal Vol. 2 No. 3 (2024): Vol. 2 No. 1 (2024): Vol. 2 No. 2 (2024): November 2024, Pages 01-77
Publisher : Mutiara Intelektual Indonesia Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61667/qr0cyd82

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This mixed-methods study examines the relationship between Project-Based Learning (PBL) and critical thinking development in undergraduate education. A total of 168 students from three universities participated in the research, which employed a mixed-methods design involving pre-post assessments, project artifact analysis, interviews, and focus group discussions across STEM, social sciences, and humanities disciplines. The results indicate statistically significant improvements in critical thinking scores, with mean scores increasing from 68.4 to 74.9 (t(167) = 8.42, p < .001, Cohen's d = 0.54). Notably, humanities students demonstrated the most substantial gains (M = 8.2 points), while analysis and evaluation subscales showed the most significant enhancements. The findings provide compelling evidence that PBL represents a sophisticated pedagogical approach for developing critical thinking skills, facilitating a dynamic, contextual process of cognitive expansion that transcends traditional disciplinary boundaries
TUGAS dan FUNGSI PENGAWAS MUTU SIPIL (Civil QC Inspector)  DALAM PENGERJAAN PEMBANGUNAN PELABUHAN (JETTY) PROYEK SUMBAWA LNG REGASDI PT. JGC INDONESIA Nafi Annury, Muhammad; Sutrisno, Djoko
Global Synthesis in Education Journal Vol. 3 No. 3 (2025): Vol. 3 No. 3, November 2025
Publisher : Mutiara Intelektual Indonesia Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61667/e1bvf861

Abstract

Authorship attribution (AA), a core task in computational linguistics, seeks to identify the author of a text based on stylistic patterns. While effective, many existing methods face a trade-off between classification accuracy and computational cost, especially when applied to large datasets. This study provides a systematic evaluation of word-level string kernel techniques as a highly efficient and accurate solution for AA. We investigate the performance of three string kernels (Spectrum, Presence Bits, and Intersection) paired with three machine learning classifiers (Support Vector Machine, Random Forest, and XGBoost). The models were tested on three distinct feature sets designed to isolate the stylistic contribution of noun phrases alongside word (n)-grams. Our findings reveal that the optimal configuration—a Support Vector Machine with a Spectrum kernel utilizing a feature set of word (n)-grams and noun phrases—achieves approximately 95% classification accuracy on the test set. This result underscores the critical role of phrasal-level syntactic information in capturing an author's unique voice. Most significantly, this word-level approach demonstrates a four- to six-fold reduction in model training time compared to a strong character-level baseline, while maintaining superior or competitive accuracy. This research concludes that word-level string kernels offer a powerful and practical framework for authorship attribution, striking an exceptional balance between high performance and computational efficiency. The method's scalability makes it highly suitable for real-world applications, including digital forensics, plagiarism detection, and large-scale textual analysis