Bibliometric Analysis of the Deep Learning Approach in Teaching the English Language

Authors

  • Janar Abdurakhimova

    Department of Teaching Theory and Methodology, Tashkent Institute of Irrigation and Agricultural Mechanisation Engineers National Research University (TIIAME NRU), Tashkent 100000, Uzbekistan
  • Muxabbat Ruzmetova

    Department of Teaching Theory and Methodology, Tashkent Institute of Irrigation and Agricultural Mechanisation Engineers National Research University (TIIAME NRU), Tashkent 100000, Uzbekistan

    Department of Business English, Banking and Finance Academy of the Republic of Uzbekistan, Tashkent 100000, Uzbekistan
  • Shaxnoza Jalolova

    Department of English Linguistics, National University of Uzbekistan named after Mirzo Ulugbek, Tashkent 100174, Uzbekistan
  • Damira Amirova

    Department of English Integrated Skills, Uzbekistan State World Languages University (UzSWLU), Tashkent 100138, Uzbekistan
  • Mastona Gozieva

    Department of Foreign Philology, Tashkent Alfraganus University, Tashkent 100027, Uzbekistan
  • Shohruza Abdurazakova

    Department of English Linguistics, National University of Uzbekistan named after Mirzo Ulugbek, Tashkent 100174, Uzbekistan
  • Nurjan Jalgasov

    Department of English Language and Literature, Nukus State Pedagogical Institute named after Ajiniyaz, Nukus, Karakalpakstan 230105, Uzbekistan

DOI:

https://doi.org/10.30564/fls.v7i7.10289
Received: 31 May 2025 | Revised: 18 June 2025 | Accepted: 27 June 2025 | Published Online: 17 July 2025

Abstract

This bibliometric analysis examines the evolving and swiftly growing domain of research on the implementation of deep learning methodologies in English language instruction. The ongoing transformation of the educational landscape by artificial intelligence (AI) has sparked significant interest in incorporating deep learning techniques among researchers and educators seeking to enhance language learning outcomes. This study utilises a selected dataset of 196 peer-reviewed articles published from 2019 to 2023, showcasing the most recent advancements in this interdisciplinary field. This global viewpoint highlights the increasing acknowledgement of deep learning's capacity to customise education, automate evaluation, and enhance communicative proficiency among English language learners. Unlike conventional machine learning techniques, deep learning provides superior skills in natural language processing, speech pattern recognition, and adaptive feedback generation, which are widely utilised in language teaching settings. This review situates itself at the intersection of applied linguistics, computer science, and educational technology, aiming to provide an in-depth understanding of how deep learning is transforming English teaching approaches. The study enhances understanding of the field's evolution by identifying research patterns, prominent authors, significant publication locations, and funding trends. Moreover, it highlights domains that are yet inadequately examined, encouraging further investigation and creativity in the integration of intelligent systems into English language instruction.

Keywords:

English Language Teaching; Deep Learning; Bibliometric Analysis; Web of Science; Citation Analysis

References

[1] LeCun, Y., Bengio, Y., Hinton, G., 2015. Deep learning. Nature. 521(7553), 436–444. DOI: https://doi.org/10.1038/nature14539

[2] Litjens, G., Kooi, T., Bejnordi, B.E., et al., 2017. A survey on deep learning in medical image analysis. Medical Image Analysis. 42, 60–88. DOI: https://doi.org/10.1016/j.media.2017.07.005

[3] Esteva, A., Robicquet, A., Ramsundar, B., et al., 2019. A guide to deep learning in healthcare. Nature Medicine. 25(1), 24–29. DOI: https://doi.org/10.1038/s41591-018-0316-z

[4] Wu, Y., Schuster, M., Chen, Z., et al., 2016. Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation. arXiv. DOI: https://doi.org/10.48550/arXiv.1609.08144

[5] Schmidhuber, J., 2015. Deep learning in neural networks: An overview. Neural Networks. 61, 85–117. DOI: https://doi.org/10.1016/j.neunet.2014.09.003

[6] Zawacki-Richter, O., Marín, V.I., Bond, M., et al., 2019. Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education. 16(1), 1–27. DOI: https://doi.org/10.1186/s41239-019-0171-0

[7] Luo, N., 2016. Corrigendum to “Supporting research writing: Roles and challenges in multilingual settings, V. Matarese (Ed.). Chandos Publishing, Oxford, (2013)” [Journal of Second Language Writing. 30 (December) (2015) 89–91]. Journal of Second Language Writing. 31, 70. DOI: https://doi.org/10.1016/j.jslw.2016.02.001

[8] Marton, F., Säaljö, R., 1976. On qualitative differences in learning—ii Outcome as a function of the learner's conception of the task. British Journal of Educational Psychology. 46(2), 115–127. DOI: https://doi.org/10.1111/j.2044-8279.1976.tb02304.x

[9] Ramsden, P., 1988. Context and Strategy. In: Schmeck, R.R. (ed.). Learning Strategies and Learning Styles. Springer US: Boston, MA, USA. pp. 159–184.

[10] Evans, B., Honour, L., 1997. Getting Inside Knowledge: the application of Entwistle’s model of surface/deep processing in producing open learning materials. Educational Psychology. 17(1–2), 127–139.

[11] Biggs, J.B., Collis, K.F., 1982. Evaluating the Quality of Learning. Academic Press: New York, USA. DOI: https://doi.org/10.1016/C2013-0-10375-3

[12] Gu, F., 2021. On Deep Learning-Based Synthesis of Language Training and Humanistic Education in College English Teaching. Open Access Library Journal. 8(6), 1–10.

[13] He, L., Li, J., 2005. Promoting deep learning for students (in Chinese) in Computer Teaching and Learning. Modern Teaching. 29–30.

[14] Cui, Y., 2017. Educational background to deep learning. People’s education. 20, 43–48.

[15] Czerkawski, B., Lyman, E., 2014. Editorial: Horizon Report 2014 and Current Status of E-Learning. Issues and Trends in Educational Technology. 2(2). Available from: https://journals.uair.arizona.edu/index.php/itet/article/view/18392/18101 (cited 25 June 2025).

[16] Papaja, K.L., Świątek, A., Mielnik, K., 2019. Deep learning (deepening the learning process) from the perspective of analyzing the needs of students of English as a foreign language [Deep learning (pogłębianie procesu uczenia się) z perspektywy analizy potrzeb studentów języka angielskiego jako obcego, in Polish]. Forum Filologiczne Ateneum. 1(7), 301–320. DOI: https://doi.org/10.36575/2353-2912/1(7)2019.301

[17] Donthu, N., Kumar, S., Mukherjee, D., et al., 2021. How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research. 133, 285–296. DOI: https://doi.org/10.1016/j.jbusres.2021.04.070

[18] Aria, M., Cuccurullo, C., 2017. bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics. 11(4), 959–975. DOI: https://doi.org/10.1016/j.joi.2017.08.007

[19] Zupic, I., Cater, T., 2013. Bibliometric Methods in Management and Organization: A Review. Academy of Management Proceedings. 2013(1), 13426. DOI: https://doi.org/10.5465/ambpp.2013.13426abstract

[20] Simonova, I., 2019. Autonomous and Collaborative e-Learning in English for Specific Purposes. In: Pappas, I., Mikalef, P., Dwivedi, Y., et al. (eds.). Digital Transformation for a Sustainable Society in the 21st Century. Springer: Cham, Switzerland. pp. 609–620.

[21] Rudneva, M., Valeeva, N., Faizi, R., et al., 2019. CORPUS-BASED APPROACH TO ESP PEDAGOGY IN TERTIARY EDUCATION. Proceedings of the 12th annual International Conference of Education, Research and Innovation (ICERI2019); 11–13 November 2019; Seville, Spain. pp. 1597–1600.

[22] Rudneva, M., Valeeva, N., Nigmatzyanova, Y., et al., 2019. SELF-ASSESSMENT FOR ADDRESSING INDIVIDUAL FOSSILIZED ERRORS WITH ADVANCED L2 LEARNERS. Proceedings of the 13th International Technology, Education and Development Conference (INTED2019); 11–13 March 2019; Valencia, Spain. pp. 5099–5102.

[23] Nigmatzyanova, Y., Valeeva, N., Rudneva, M., et al., 2019. MATERIALS FOR TEACHING ENGLISH FOR SPECIFIC PURPOSES AS A MEANS TO ENHANCE STUDENTS’ ETHICAL AWARENESS. A CASE STUDY OF EVALUATION OF “AN ECOMODERNIST MANIFESTO” BY JOHN ASAFU-ADJAYE ET AL. Proceedings of the 13th International Technology, Education and Development Conference (INTED2019); 11–13 March 2019; Valencia, Spain. pp. 6213–6218.

[24] Nigmatzyanova, Y., Valeeva, N., Rudneva, M., et al., 2019. Developing speaking skills through cartoons in English for Specific Purposes (ESP) classes. A case study of ecological faculty students. Proceedings of the 13th International Technology, Education and Development Conference (INTED2019); 11–13 March, 2019; Valencia, Spain. pp. 4091–4094.

[25] Didenko, I., Zhukova, N., 2021. TEACHING WRITING AND ERROR CORRECTION IN AN ENGLISH FOR SPECIFIC PURPOSES CLASSROOM IN 2014-2020 IN UKRAINE. Journal of teaching English for specific and academic purposes. 9(3). DOI: https://doi.org/10.22190/JTESAP2103363D

[26] Rudneva, M., Valeeva, N., Faizi, R., et al., 2019. TED Talks for enhancing listening comprehension skills at university level. Proceedings of the 13th International Technology, Education and Development Conference;11–13 March 2019; Valencia, Spain. pp. 5070–5073.

[27] Avila-Cabrera, J., Rodríguez-Arancón, P., 2021. The use of active subtitling activities for students of Tourism in order to improve their English writing production. Ibérica. 41, 155–180.

[28] Faltynkova, L., Simonova, I., Kostolanyova, K., et al., 2021. Learners’ Preferences in ESP Instruction for Higher Medical Staff. In: Auer, M., Ruutmann, T. (eds.). Educating Engineers for Future Industrial Revolutions. Springer, Cham, Switzerland. pp. 655–662.

[29] Lezhneva, E., Nikolaeva, N., 2019. FACING ENGINEERING DISCOURSE: PEER FEEDBACK IN DEVELOPING TECHNICAL WRITING SKILLS. Proceedings of the 12th annual International Conference of Education, Research and Innovation; 11–13 November, 2019; Seville, Spain. pp. 5605–5609.

[30] Synekop, O., 2023. Hackathon in Differentiated English for Specific Purposes Instruction of Information Technology Students. Multidisciplinary Journal of Education, Social and Technological Sciences. 10(2), 77–93.

[31] Zhukova, N., 2019. ENHANCING ‘SOFT SKILLS’ IN THE ENGLISH FOR SPECIFIC PURPOSES COURSE WHEN RECORDING A VIDEO. In: Beseda, J., Rohlikova, L., Duffek, V. (eds.). E-learning: Unlocking the Gate to Education around the Globe. Centre for Higher Education Studies: Prague, Czech Republic. pp. 108–117.

[32] Rose, H., Curle, S., Aizawa, I., et al., 2020. What drives success in English medium taught courses? The interplay between language proficiency, academic skills, and motivation. Studies in Higher Education. 45(11), 2149–2161. DOI: https://doi.org/10.1080/03075079.2019.1590690

[33] Xie, W., Curle, S., 2022. Success in English Medium Instruction in China: significant indicators and implications. International Journal of Bilingual Education and Bilingualism. 25(2), 585–597. DOI: https://doi.org/10.1080/13670050.2019.1703898

[34] Ho, Y., 2020. Communicative language teaching and English as a foreign language undergraduates’ communicative competence in Tourism English. Journal of Hospitality, Leisure, Sport & Tourism Education. 27, 100271. DOI: https://doi.org/10.1016/j.jhlste.2020.100271

[35] Tai, H., 2019. AN ESP APPROACH TO TEACHING NURSING NOTE WRITING TO UNIVERSITY NURSING STUDENTS. Proceedings of the 11th International Conference on Education and New Learning Technologies; 1–3 July, 2019; Palma, Spain. pp. 10643–10643.

[36] Gaffas, Z., 2023. Students’ perceptions of e-learning ESP course in virtual and blended learning modes. Education and Information Technologies. 28(8), 10329–10358.

[37] Simonova, I., 2019. The Preference of Electronic, or Printed Materials Revisited. In: Cheung, S., Lee, L., Simonova, I., et al. (eds.). Blended Learning: Educational Innovation for Personalized Learning. Springer, Cham, Switzerland. pp. 105–116.

[38] Karpushyna, M., Bloshchynskyi, I., Zheliaskov, V., et al., 2019. Warm-Up as a Means of Fostering Target-Language Performance in a Particular English Class. Revista Romaneasca pentru Educatie Multidimensionala. 11(2), 141–159.

[39] Chan, C., 2021. University graduates’ transition into the workplace: How they learn to use English for work and cope with language-related challenges. System. 100, 102541. DOI: https://doi.org/10.1016/j.system.2021.102541

[40] Cheng, A., 2019. Examining the ‘applied aspirations’ in the ESP genre analysis of published journal articles. Journal of English for Academic Purposes. 38, 36–47.

[41] Liu, F., 2024. Implementing E-Learning in English Translation Teaching Using Deep Learning Models and Output-Oriented Methods. Computer-Aided Design and Applications. 21, 219–235.

[42] Rudneva, M., Valeeva, N., Zakirova, Y., et al., 2020. Flipped classroom approach to teaching ESP listening. In Proceedings of the 14th International Technology, Education and Development Conference, Valencia, Spain, 2–4 March 2020; pp. 7499–7502.

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How to Cite

Abdurakhimova, J., Ruzmetova, M., Jalolova, S., Amirova, D., Gozieva, M., Abdurazakova, S., & Jalgasov, N. (2025). Bibliometric Analysis of the Deep Learning Approach in Teaching the English Language. Forum for Linguistic Studies, 7(7), 821–838. https://doi.org/10.30564/fls.v7i7.10289

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Vol. 7 , Iss. 7 (July 2025): In progress

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Copyright © 2025 Janar Abdurakhimova, Muxabbat Ruzmetova, Shaxnoza Jalolova, Damira Amirova, Mastona Gozieva, Shohruza Abdurazakova, Nurjan Jalgasov

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This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.