EFL Learners’ Use of Data-driven Learning and their Attitudes in the Improvement of English-speaking Skills

Authors

  • Zhaoyi Pan

Keywords:

data-driven learning; vocabulary production; English speaking; learners of English; productive skill

Abstract

The use of data-driven learning (DDL) to improve the English-speaking skills of learners of English as a foreign language (EFL) remains rare. Hence, this research examines whether two types of DDL – namely, hands-on DDL using the computer software directly and hands-off DDL using paper-based materials – can improve vocabulary production in the English speaking of EFL learners. The EFL learners’ attitudes toward both types of DDL were also examined. A total of 45 Thai EFL learners were involved in this research; they were divided equally into two experimental groups, one using hands-on DDL and hands-off DDL, and one control group. A questionnaire and interviews were used to examine the EFL learners’ attitudes toward DDL and a paired sample t-test and a one-way analysis of variance (ANOVA) were conducted. The results reveal that both hands-on and hands-off DDL approaches significantly improved vocabulary production in the EFL learners’ spoken English. In addition, the hands-on DDL had a significant effect on the quantity (sig. = .000, p <0.05), accuracy (sig. = .000, p <0.05), and complexity (sig. = .000, p <0.05) of the participants’ vocabulary production, while the hands-off DDL only had a significant effect on the accuracy (sig. = .000, p <0.05) of vocabulary production. Furthermore, although the EFL learners had relatively positive attitudes toward DDL, less enjoyable experiences were also noted. Experiences of boredom and stress while using DDL were reported, and the participants did not consider DDL to be suitable for all EFL learners.

https://doi.org/10.26803/ijlter.23.8.6

References

Abdelmageed, N. A. T., & Omer, M. A. A. (2020). The effectiveness of using communicative language teaching approach (CLT) in developing students’ speaking skills from teachers’ perceptions. European Journal of English Language Teaching, 5(3), 88-102. http://dx.doi.org/10.46827/ejel.v0i0.3044

Anthony, L. (2023). AntConc (MacOS 10/11, 4.1.3 Version) [Computer Software]. Waseda University. https://www.laurenceanthony.net/software/antconc/

Boulton, A., & Cobb, T. (2017). Corpus use in language learning: A meta-analysis. Language Learning, 67(2), 348-393. https://doi.org/10.1111/lang.12224

Corino, E., & Onesti, C. (2019). Data-driven learning: A scaffolding methodology for CLIL and LSP teaching and learning. Frontiers in Education, 4(7), https://doi.org/10.3389/feduc.2019.00007

Council of Europe. (2020). Common European Framework of Reference for Languages: Learning, teaching, assessment-Companion volume. Council of Europe Publishing. www.coe.int/lang-cefr

Davies, M. (2008). The Corpus of Contemporary American English (COCA). [Data set]. https://www.english-corpora.org/coca/

Hirschi, K., & Kang, A. O. (2024). Data-driven learning for pronunciation: Perception and production of lexical stress and prominence in academic English. TESOL Quarterly (in press), 1-27. https://doi.org/10.1002/tesq.3302

Hui, S. M., & Yunus, M. M. (2023). Revisiting communicative language teaching approach in teaching ESL speaking skills. Journal of Language Teaching and Research, 14(6), 1515-1523. https://doi.org/10.17507/jltr.1406.09

Karpenko-Seccombe, T. (2023). Data-driven learning: Aiming at the bigger picture. Nordic Journal of English Studies, 22(1), 144-181. https://doi.org/10.35360/njes.798

K?z?l, A. S?. (2023). Data-driven learning: English as a foreign language writing and complexity, accuracy and fluency measures. Journal of Computer Assisted Learning, 39(7), 1382-1395. https://doi.org/10.1111/jcal.12807

Lay, K. J., & Yavuz, M. A. (2020). Data-driven learning of academic lexical bundles below the C1 level. Language Learning & Technology, 24(3), 176-193. http://hdl.handle.net/10125/44741

Lee, P., & Lin, H. (2019). The effect of the inductive and deductive data-driven learning (DDL) on vocabulary acquisition and retention. System, 81, 14-25. https://doi.org/10.1016/j.system.2018.12.011

Lin, M. H. (2021). Effects of data-driven learning on college students of different grammar proficiencies: A preliminary empirical assessment in EFL classes. SAGE Open, 11(3), 1-15. https://doi.org/10.1177/21582440211029936

Lusta, A., Demirel, Ö., & Mohammadzadeh, B. (2023). Language corpus and data driven learning (DDL) in language classrooms: A systematic review. Heliyon, 9, 1-19. https://doi.org/10.1016/j.heliyon.2023.e22731

Mizumoto, A. (2023). Data-driven learning meets generative AI: Introducing the framework of metacognitive resource use. Applied Corpus Linguistics, 3(3), 1-5. https://doi.org/10.1016/j.acorp.2023.100074

Muftah, M. (2023). Data-driven learning (DDL) activities: do they truly promote EFL students’ writing skills development? Education and Information Technologies, 28(10), 13179-13205. https://doi.org/10.1007/s10639-023-11620-z

Murad, T., Assadi, J., & Badarni, H. (2023). Digital storytelling and EFL speaking skill improvement. Journal of Language Teaching and Research, 14(5), 1189-1198. https://doi.org/10.17507/jltr.1405.06

O’Keeffe, A. (2021). Data-driven learning – a call for a broader research gaze. Language Teaching, 54(2), 259-272. https://doi.org/10.1017/S0261444820000245

Pérez-Paredes, P., Guillamón, C. O., de Vyver, J. V., Meurice, A., Jiménez, P. A., Conole, G., & Hernández, P. S. (2019). Mobile data-driven language learning: Affordances and learners’ perception. System, 84, 1-15. https://doi.org/10.1016/j.system.2019.06.009

Pratiwi, W. R., Kuswoyo, H., Puspitasari, M., Juhana, J., & Bachtiar, B. (2024). Driving to communicative approach: the innovative teaching speaking methods in Indonesian English immersion program. International Journal of Evaluation and Research in Education, 13(1), 626-634. http://doi.org/10.11591/ijere.v13i1.25420

Saeedakhtar, A., Bagerin, M., & Abdi, R. (2020). The effect of hands-on and hands-off data-driven learning on low-intermediate learners’ verb-preposition collocations. System, 91, 1-14. https://doi.org/10.1016/j.system.2020.102268

Tosun, S., & Sofu, H. (2023). The effectiveness data-driven vocabulary learning: Hands-on concordancing through a pedagogical corpus. Journal of Language and Education, 9(3), 177-191. https://doi.org/10.17323/jle.2023.12426

Ueno, S., & Takeuchi, O. (2023). Effective corpus use in second language learning: A meta-analytic approach. Applied Corpus Linguistics, 3(3), 1-11. https://doi.org/10.1016/j.acorp.2023.100076

Zare, J., & Delavar, K. A. (2022). Enhancing English learning materials with data- driven learning: a mixed-methods study of task motivation. Journal of Multilingual and Multicultural Development (in press), 1-19. https://doi.org/10.1080/01434632.2022.2134881

Zare, J., Karimpour, S., & Delavar, K. A. (2022). The impact of concordancing on English learners’ foreign language anxiety and enjoyment: An application of data-driven learning. System, 109, 1-12. https://doi.org/10.1016/j.system.2022.102891

Zhang, J. (2022). Data-driven learning teaching model of college English based on mega data analysis. Scientific Programming (Special issue), 1-9. https://doi.org/10.1155/2022/3490594

Zhussupova, R., & Shadiev, R. (2023). Digital storytelling to facilitate academic public speaking skills: case study in culturally diverse multilingual classroom. Journal of Computers in Education, 10(3), 499-526. https://doi.org/10.1007/s40692-023-00259-x

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Published

2024-08-30