The Use of Artificial Intelligence to Promote Autonomous Pronunciation Learning: Segmental and Suprasegmental Features Perspective

Senowarsito Senowarsito, Sukma Nur Ardini

Abstract


The study aimed at investigating the effects of autonomous pronunciation learning using AI as well as the experiences of autonomous pronunciation learning using AI by higher level students. Explanatory sequential mixed-method research using both quantitative and qualitative methods was employed within thirty-two students from Universitas PGRI Semarang's first-year students serving as the sample. Assessments, interviews, and an evaluation of instructional materials were used as the instruments. Through pre- and post-testing, quantitative analysis was used to evaluate the students’ pronunciation proficiency. Quantitative data analysis was done using SPSS. However, a qualitative analysis was used to review the interview. To bolster the findings of the tests, it was descriptively examined. After the treatments using an AI based application named ELSA, there was a significant correlation between the use of AI and autonomous pronunciation learning. However, ELSA has certain shortcomings. It appears to be primarily concerned with segmental than suprasegmental features. Only intonation is available from among all the features offered to practice suprasegmental features. While students found it difficult to emphasize words, there is no other practice for suprasegmental qualities. In reality, the ELSA website states that its curriculum covers core English skills such as word stress, intonation, rhythm, listening, and conversation. As a result, the ELSA creator may take this criticism into consideration as they continue to improve their product. It implies that the creator is responsive to the concerns or suggestions of their customers or users, which can contribute to the ongoing development and success of the product.

Keywords


Artificial Intelligence; autonomous learning, segmental features; suprasegmental features; higher education

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References


Anderson‐Hsieh, J., Johnson, R., & Koehler, K. (1992). The Relationship Between Native Speaker Judgments of Nonnative Pronunciation and Deviance in Segmentais, Prosody, and Syllable Structure. Language Learning, 42(4), 529–555. https://doi.org/10.1111/J.1467-1770.1992.TB01043.X

Ardini, S. N., WL, M. Y., & Ouwpoly, N. L. (2016). Error Analysis of Phonetic Fossilization Uttered by English Department Students University of PGRI Semarang. Lensa: Kajian Kebahasaan, Kesusastraan, dan Budaya, 6(1), 1-8. https://jurnal.unimus.ac.id/index.php/lensa/article/view/1917

Baker, T., Smith with Nandra Anissa, L., Sheehan, K., Ward, K., Waters, A., Berditchevskaia, A., Van Den Berg, C., Campbell, N., Candsell, O., Casasbuenas, J., Cinnamon, J., Copeland, E., Duffy, E., Hannon, C., John, J., Grant, J., Klinger, J., Latham, M., Macken, C., … Ward-Dyer, G. (2019). Educ-AI-tion Rebooted? Exploring the future of artificial intelligence in schools and colleges. www.nesta.org.uk

Becker, K., & Edalatishams, I. (2019). ELSA Speak Accent Reduction [Review]. Pronunciation in Second Language Learning and Teaching Proceedings, 10(1), 434. https://www.iastatedigitalpress.com/psllt/article/id/15397/

Britannica, T. Editors of Encyclopedia (2020, February 28). suprasegmental. Encyclopedia Britannica. https://www.britannica.com/topic/suprasegmental

Carlet, A., & De Souza, H. K. (2018). Improving L2 Pronunciation Inside and Outside the Classroom: Perception, Production and Autonomous Learning of L2 Vowels. In Ilha do Desterro (Vol. 71, Issue 3, pp. 99–123). Universidade Federal de Santa Catarina. https://doi.org/10.5007/2175-8026.2018v71n3p99

Chassignol, M., Khoroshavin, A., Klimova, A., & Bilyatdinova, A. (2018). Artificial Intelligence Trends in Education: A Narrative Overview. Procedia Computer Science, 136, 16–24. https://doi.org/10.1016/J.PROCS.2018.08.233

Creswell, J. (2012). Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research. http://nuir.nkumbauniversity.ac.ug/handle/20.500.12383/985

Creswell, W. J. and, & Creswell, D. J. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 5th Edition: by John W. Creswell and J. David Creswell, Los Angeles, CA: SAGE, 2018, $38.34, 304pp., ISBN: 978-1506386706: Journal of Electronic Resources in Medical Libraries: Vol 19, No 1-2. https://www.tandfonline.com/doi/full/10.1080/15424065.2022.2046231

Derwing, T. M., & Munro, M. J. (1997). Accent, Intelligibility, and Comprehensibility: Evidence from Four L1s. Studies in Second Language Acquisition, 19(1), 1–16. https://doi.org/10.1017/S0272263197001010

Derwing, T. M., & Rossiter, M. J. (2002). ESL Learners’ Perceptions of their Pronunciation Needs and Strategies. System, 30(2), 155–166. https://doi.org/10.1016/S0346-251X(02)00012-X

Fraenkel, J. R., & Wallen, N. E. (2012). How to Design and Evaluate Research in Education.

Gashimov, E. (2023). Interference in a Language and Culture Communication. Allure Journal, 3(1), 56–63. https://doi.org/10.26877/allure.v3i1.14156

Hidayati, T., & Husna, F. (2020). Learning English from Home during the Covid-19: Investigating Learners’ Experience for Online and Autonomous Learning. 6(2). http://dx.doi.org/10.313

Holec, Henri., & Council of Europe. (1979). Autonomy and Foreign Language Learning. 53.

Huang, G., & Moore, R. K. (2023). Using Social Robots for Language Learning: Are We There Yet? Journal of China Computer-Assisted Language Learning, 3(1), 208–230. https://doi.org/10.1515/jccall-2023-0013

Hussein Banafa, F. (2008). Effects of IT on Pronunciation Impact of the Internet and Interactive Multimedia on English Pronunciation of Arab College Students Studying in the United States. VDM Verlag.

Jiang R (2022) How does Artificial Intelligence Empower EFL Teaching and Learning Nowadays? A review on Artificial Intelligence in the EFL Context. Front. Psychol. 13:1049401. Doi: 10.3389/fpsyg.2022.1049401

Joan Morley. (1999). New Developments in Speech Pronunciation Instruction. As We Speak. Newsletter of the TESOL Speech, Pronunciation, and Listening Interest Section, 1–5.

Kang, O., Rubin, D., & Pickering, L. (2010). Suprasegmental Measures of Accentedness and Judgments of Language Learner Proficiency in Oral English. The Modern Language Journal, 94(4), 554–566. https://doi.org/10.1111/J.1540-4781.2010.01091.X

Kholis, A. (2021). Elsa Speak App: Automatic Speech Recognition (ASR) for Supplementing English Pronunciation Skills. Pedagogy: Journal of English Language Teaching, 9(1), 01-14. doi:10.32332/joelt.v9i1.2723

Kruk, M. (2012). Using Online Resources in the Development of Learner Autonomy and English Pronunciation: The Case of Individual Learners. Journal of Second Language Teaching & Research, 1(2), 113–142. https://pops.uclan.ac.uk/index.php/jsltr/article/view/28

Layali, K., and Al-Shlowiy, A. (2020). Students Perceptions of E-learning for ESL/EFL in Saudi Universities at Time of Coronavirus: A Literature Review. Indonesian EFL Journal. 6, 97–108. doi: 10.25134/ieflj.v6i2.3378

Lee, J., Jang, J., & Plonsky, L. (2015). The Effectiveness of Second Language Pronunciation Instruction: A Meta-Analysis. Applied Linguistics, 36(3), 345–366. https://doi.org/10.1093/APPLIN/AMU040

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. (2016). Intelligence Unleashed: An Argument for AI in Education. http://oro.open.ac.uk/50104/1/Luckin%20et%20al.%20-%202016%20%20Intelligence%20Unleashed

Meissner, H. I., Creswell, J. W., Klassen, A. C., Clark, V. L. P., & Smith, K. C. (2011). Best Practices for Mixed Methods Research in the Health Sciences.

Ocaña-Fernández, Y., Valenzuela-Fernández, L. A., & Garro-Aburto, L. L. (2019). Inteligencia Artificial y Sus Implicaciones en la Educación Superior. Propósitos y Representaciones, 7(2). https://doi.org/10.20511/pyr2019.v7n2.274

Penny Ur. (1984). A Course in Language Teaching: Practice and Theory. Cambridge University Press.

Phuong, T. T. H. (2021). Who Should Teach English Pronunciation? Voices of Vietnamese EFL Learners and Teachers. Journal of Asia TEFL, 18(1), 125–141. https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE10649594

Popenici, S. A. D., & Kerr, S. (2017). Exploring the Impact of Artificial Intelligence on Teaching and Learning in Higher Education. Research and Practice in Technology Enhanced Learning, 12(1). https://doi.org/10.1186/s41039-017-0062-8

Reinders, H., & Pegrum, M. (2015). Supporting Language Learning on the Move. An evaluative framework for mobile language learning resources. https://www.researchbank.ac.nz/handle/10652/2991

Rifqiyah, A., Ardini, S. N., & Kusumo, A. B. P. (2021). English pronunciation application as A Media to Improve Students’ Pronunciation: The Effectiveness. Linguistics and Education Journal, 1(1), 74–85. https://doi.org/10.17576/3L-2019-2502-06

Rusmiyanto, R., Huriati, N., Fitriani, N., Tyas, N., Rofi’i, A., & Sari, M. (2023). The Role of Artificial Intelligence (AI) In Developing English Language Learner’s Communication Skills. Journal on Education, 6(1), 750- 757. https://doi.org/10.31004/joe.v6i1.2990

Sariani, Miladiyenti, F., Rozi, F., Haslina, W., & Marzuki, D. (2022). Incorporating Mobile-based Artificial Intelligence to English Pronunciation Learning in Tertiary-level Students: Developing Autonomous Learning. International Journal of Advanced Science Computing and Engineering,4(3), December 2022, pp. 220-232.

Senowarsito, & Ardini, S. N. (2016). English Phonology (For Learners of English as a Foreign Language). Universitas PGRI Semarang.

Senowarsito, & Ardini, S. N. (2019). Phonological Fossilization of EFL Learners: The Interference of Phonological and Orthographic System of L1 Javanese. The Southeast Asian Journal of English Language Studies, 3(2), 74–85. https://doi.org/10.17576/3L-2019-2502-06

Tahereen, T. (2015). Challenges in Teaching Pronunciation at Tertiary Level in Bangladesh. International Journal of English Language & Translation Studies, 3(1), 9–20. http://www.eltsjournal.orghttp://www.eltsjournal.org

Tiara, A. F., Ardini, S. N., & Nugrahani, D. (2020). Blended Learning in Pronunciation Classroom for Higher Education: Students’ Perception. E-Structural (English Studies on Translation, Culture, Literature, and Linguistics), 3(02), 145-156. DOI: https://doi.org/10.33633/es.v3i02.4414

Tuomi, I. (2018). The Impact of Artificial Intelligence on Learning, Teaching, and Education. Policies for the future, Eds. Cabrera, M., Vuorikari, R & Punie, Y., EUR 29442 EN, Publications Office of the European Union, Luxembourg, ISBN 978-92-79-97257-7, doi:10.2760/12297, JRC113226.

Xie, Z. (2020). Effectiveness of Autonomous Learning Materials for Students during the COVID-19 Pandemic: A Case Study of the Daxie Second Elementary School in Ningbo, Zhejiang, China. Science Insights Education Frontiers, 6(1), 613–624. https://doi.org/10.15354/sief.20.or023

Yang. (2000). Self-study textbook design manual (pp. 3–5). Psychological Publishing House.

Zhang, F., & Yin, P. (2009). A Study of Pronunciation Problems of English Learners in China.

Zhang, H., & Zhang, L. (2022). Introducing Chinese Linguistics: A Handbook for Chinese Language Teachers and Learners. Studies in Chinese Language and Discourse. John Benjamins Publishing Company. https://benjamins.com/content/home




DOI: http://dx.doi.org/10.21093/ijeltal.v8i2.1452

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