Mohammad Amini Farsani | Applied Linguistics | Best Researcher Award

Dr. Mohammad Amini Farsani | Applied Linguistics | Best Researcher Award

Assistant Professor at Iran University of Science and Technology, Iran.

Dr. Mohammad Amini Farsani is an Assistant Professor of Applied Linguistics at Iran University of Science and Technology, Tehran ๐Ÿ‡ฎ๐Ÿ‡ท. His research centers on L2 argumentative writing, TEFL, and methodological synthesis. ๐Ÿ“˜ He has authored numerous impactful publications in top-tier journals including Journal of Second Language Writing, Language Teaching, Argumentation, and System. ๐Ÿง  He leads innovative studies involving mixed-methods research, sentiment analysis, and academic writing behavior among EFL learners. As a mentor, he actively supports graduate research on meta-analysis, argument mining, and feedback mechanisms. โœ๏ธ His work bridges conceptual theory with practical implications for global second language education.

Professional Profile:

Scopus

ORCID

Suitability for Best Researcher Award โ€“ Dr. Mohammad Amini Farsani

Dr. Mohammad Amini Farsani is highly suitable for the Best Researcher Award due to his sustained, innovative, and high-impact contributions to the fields of Applied Linguistics and L2 academic writing. As an Assistant Professor at Iran University of Science and Technology, he has demonstrated academic leadership through a strong publication record, international recognition, and methodological advancements. His research not only deepens theoretical understanding but also offers practical applications for language education, setting a benchmark for scholarly excellence in TEFL and L2 writing studies.

๐ŸŽ“ Education and Experience

  • ๐ŸŽ“ Ph.D. in Applied Linguistics โ€“ Specialized in L2 writing and research methodology

  • ๐ŸŽ“ Masterโ€™s in TEFL โ€“ Focused on English language education and pedagogy

  • ๐Ÿ‘จโ€๐Ÿซ Assistant Professor, Iran University of Science and Technology โ€“ Current position

  • ๐Ÿ“š Graduate Research Supervisor โ€“ Supervises MA theses in L2 writing and applied linguistics

  • ๐Ÿ“ Editorial and Peer Review Experience โ€“ Regular contributor and reviewer for international journals

๐Ÿ“ˆ Professional Development

Dr. Farsani has demonstrated significant professional growth through publishing cutting-edge research in L2 writing and applied linguistics. ๐Ÿ“‘ He has refined his expertise in meta-research, mixed-methods design, and argumentation models such as Toulmin and pragma-dialectics. ๐Ÿ” He collaborates on cross-cultural studies and supervises student research on emerging topics like sentiment analysis and emotion regulation in academic writing. ๐Ÿค He continuously enhances his academic influence by contributing to encyclopedic entries and state-of-the-art reviews in high-impact journals, actively participating in scholarly discourse and presenting at conferences worldwide. ๐ŸŒ His work exemplifies scholarly rigor and pedagogical relevance.

๐Ÿ”ฌ Research Focus

Dr. Farsaniโ€™s research primarily focuses on Second Language (L2) Argumentative Writing in the context of Teaching English as a Foreign Language (TEFL). โœ๏ธ His work delves into argumentation theory, academic discourse, and written corrective feedback, with a particular emphasis on applying pragma-dialectic and Toulmin models across cultural contexts. ๐ŸŒ He also explores meta-research, mixed-methods synthesis, and quantitative/qualitative paradigms, using bibliometrics and scientometric approaches to map research trends. ๐Ÿ“Š His integrated approach helps bridge pedagogical theory and classroom application, contributing significantly to the improvement of L2 academic writing instruction and methodology. ๐Ÿ“š๐Ÿงฉ

๐Ÿ† Awards and Honors

  • ๐Ÿ… Publication in Top Journals โ€“ Journal of Second Language Writing, Argumentation, Language Teaching

  • ๐Ÿ“˜ State-of-the-art Article โ€“ Mixed Methods Research in Language Teaching (Cambridge)

  • ๐Ÿฅ‡ Featured Researcher โ€“ Recognized for methodological innovations in L2 writing and applied linguistics

  • ๐Ÿง‘โ€๐Ÿซ Graduate Student Mentorship Recognition โ€“ Supervising impactful MA theses and student publications

  • ๐Ÿ“– Contributor to Prestigious Encyclopedias โ€“ Entry on mixed methods in The Encyclopedia of Applied Linguistics (John Wiley)

Publication Top Notes

๐Ÿ“˜ 1. Language Mindset, Anxiety, and Proficiency: What Does Path Analytic Approach Indicate?

Authors: Mohammad Amini Farsani, Shadi Sadat Seyedshoja
Journal: The Asia-Pacific Education Researcher (2025)
DOI: 10.1007/s40299-024-00857-y
Citations: 1
๐Ÿ” Summary: This study utilizes a path analytic approach to explore how learnersโ€™ language mindsets (fixed vs. growth), language anxiety, and proficiency are interrelated. The results suggest that growth mindset negatively predicts anxiety and positively influences proficiency, providing critical implications for mindset-based language instruction.

๐Ÿ“˜ 2. Tracing Sampling in Applied Linguistics: The Case of Quantitative Research Approach

Authors: Mohammad Amini Farsani, Reihaneh Barzegar
Journal: Quality & Quantity (2025)
DOI: 10.1007/s11135-024-01929-3
Citations: 1
๐Ÿ” Summary: This article critically reviews sampling strategies in quantitative applied linguistics research, highlighting methodological inconsistencies and advocating for improved reporting practices. It proposes a framework for enhancing sample representativeness and transparency in future studies.

๐Ÿ“˜ 3. Topical Trends and Research Frontiers of Applied Linguistics Research Articles with Different Methodological Orientations: A Bibliometric-Synthetic Review

Authors: Mohammad Amini Farsani, Hamid R. Jamali
Book Chapter: 2024 | Springer
DOI: 10.1007/978-3-031-51726-6_4
Citations: 2
๐Ÿ” Summary: This bibliometric-synthetic review examines methodological diversity in applied linguistics research articles from 2000 to 2023. It identifies key thematic trends and highlights underexplored research frontiers, offering guidance for future scholarly directions.

๐Ÿ“˜ 4. Exploring Emotion Regulation Strategies in an EFL Argumentative Writing Course: The Case of Iranian MA Students in TEFL

Authors: Mohammad Amini Farsani, Delaram Oroomieh, Khashayar Ramezanpour
Journal: Current Psychology (2024)
DOI: 10.1007/s12144-024-07081-3
Citations: 0
๐Ÿ” Summary: Focusing on emotion regulation in academic writing, this study explores how Iranian MA TEFL students manage emotional challenges in argumentative writing tasks. It identifies adaptive strategies like cognitive reappraisal as key to better writing performance and emotional well-being.

๐Ÿ“˜ 5. Examining Research Motivation, Self-Efficacy, and Anxiety in TEFL Graduate Students: A Structural Equation Modelling Approach

Authors: Mohammad Amini Farsani, Babak Daneshvar Ghorbani
Journal: Research in Post-Compulsory Education (2024)
DOI: 10.1080/13596748.2024.2371648
๐Ÿ” Summary: Using structural equation modeling (SEM), this paper investigates how TEFL graduate studentsโ€™ research motivation and self-efficacy affect their anxiety levels. It offers pedagogical insights for supporting student researchers in higher education.

๐Ÿ“˜ 6. Mixed-Methods Research in Applied Linguistics: Charting the Progress Through the Second Decade of the Twenty-First Century

Authors: A. Mehdi Riazi, Mohammad Amini Farsani
Journal: Language Teaching (2024)
DOI: 10.1017/S0261444823000332
Citations: 17
๐Ÿ” Summary: This comprehensive review charts the evolution of mixed-methods research in applied linguistics from 2011โ€“2020. It evaluates the quality of design, integration, and reporting practices, emphasizing the growing significance of methodological pluralism in the field.

๐Ÿ“˜ 7. Collaboration Network of Applied Linguistics Research Articles with Different Methodological Orientations

Authors: Mohammad Amini Farsani, Hamid R. Jamali
Journal: Studies in Second Language Learning and Teaching (2023)
DOI: 10.14746/ssllt.40214
Citations: 1
๐Ÿ” Summary: This network analysis explores co-authorship patterns in applied linguistics articles with different methodological approaches (quantitative, qualitative, and mixed methods), revealing distinct collaborative clusters and trends in knowledge production.

Conclusion

Dr. Farsani’s scholarly achievements, methodological rigor, and mentorship make him a top-tier candidate for the Best Researcher Award. His research is not only prolific and well-cited but also strategically positioned to influence both academic theory and practical instruction in global language education. He exemplifies the qualities of a visionary researcher and academic leader, deserving this prestigious recognition.

Boyuan REN | Linguistics | Best Researcher Award

Ms. Boyuan REN | Linguistics | Best Researcher Award

Hong Kong at The Education University of Hong Kong, Hong Kong.

Boyuan Ren is a dedicated researcher specializing in linguistics, AI-driven language learning, and educational technology. He holds a Masterโ€™s degree from Nanyang Technological University (NTU) and a Bachelor’s degree from Xiโ€™an Jiaotong University (XJTU). His research focuses on technology-enhanced language learning, AI applications in education, and corpus-based studies. Ren has actively contributed to multiple international conferences and has published in peer-reviewed journals. With expertise in SPSS, Python, R, HTML, and Eyetracking, he integrates data science and linguistics to enhance language education. He also has experience in editing, translation, and conference organization.

Professional Profile:

ORCID Profile

Education & Experience ๐ŸŽ“๐Ÿ’ผ

  • 2022-2023 โ€“ Masterโ€™s Degree, Nanyang Technological University (NTU) ๐Ÿ“š
  • 2017-2021 โ€“ Bachelorโ€™s Degree, Xiโ€™an Jiaotong University (XJTU) ๐ŸŽ“
  • Research Experience ๐Ÿซ
    • Researcher at Department of Linguistics and Modern Language Studies, EdUHK ๐Ÿ”ฌ
    • Focus on AI-driven language learning, corpus-based approaches, and pedagogical research ๐Ÿ–ฅ๏ธ
  • Teaching & Work Experience ๐Ÿข
    • 2023 โ€“ English Editorial Intern, Foreign Language Teaching and Research Press ๐Ÿ“–
    • 2019-2020 โ€“ Translator, Enlighten International Technology & Culture Co. Ltd ๐ŸŒ

Professional Development ๐Ÿš€

Boyuan Ren is deeply engaged in technology-enhanced language learning and AI integration in education. As a conference secretary for TeLLT & CoLLT 2024, he plays a key role in organizing discussions on AI-assisted language education. His research extends to citizenship education, critical discourse analysis, and machine-assisted translations, emphasizing multicultural perspectives. Ren has showcased his work at international conferences, including The 8th FLLT 2024 Conference and The 3rd International Conference on Data Science and L2 Studies. His strong computational skills in SPSS, Python, R, and VOSviewer make him a valuable contributor to linguistic data science and AI-based language pedagogy.

Research Focus ๐Ÿ”

Boyuan Renโ€™s research centers on technology-enhanced language learning, corpus linguistics, and AI-driven education. He explores how Generative AI can enhance academic writing, linguistic analysis, and language teacher training. His study on Chinese Poem Translation with Large Language Models highlights his expertise in AI-assisted literary translation. Additionally, his research on critical discourse analysis in citizenship education textbooks reflects his interest in language, identity, and multicultural perspectives. Ren is also engaged in developing professional communities for language teachers to enhance their Technological Pedagogical Content Knowledge (TPACK). His work bridges data science, linguistics, and pedagogy to create effective learning solutions.

Awards & Honors ๐Ÿ†

  • 2024 โ€“ Best Poster Award at The 3rd International Conference on Data Science and L2 Studies ๐Ÿฅ‡
  • Conference Secretary at TeLLT & CoLLT 2024 ๐ŸŽค
  • Published Research in Education Sciences (2024) ๐Ÿ“„
  • Presented at International Conferences in Thailand, China, and Hong Kong ๐ŸŒ
  • Fluent in Mandarin Chinese (Native), English, and Basic German (A1) ๐ŸŒ

Publication Top Notes

Authors: WANG, Lixun; Boyuan REN
Citations: Not available yet
Year: 2024
DOI: 10.3390/educsci14121329