EFL university students’ self-directed language learning with ICT: a structural equation modelling approach*

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https://doi.org/10.25267/Hachetetepe.2024.i29.2205

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2205
Published: 13-10-2024
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Abstract

The growth of information and communication technology (ICT) can enhance students' self-directed language learning (SDLL). Language learning in online settings have determined positive correlations between self-directed learning behaviour and academic achievement. This study was conducted to examine factors influencing self-directed language learning with ICT. A quantitative design was applied, which involved 1,022 pre-service teachers of English department from nine universities in Indonesia. A questionnaire was employed to collect the data, and the proposed hypotheses were examined using PLS-SEM. The PLS-SEM analysis demonstrated that the attitude towards the use of ICT mediates the influence of ICTSE, OCSE, FC, and SN on SDLLICT. The results indicated that students' attitude is the most significant variable in enhancing self-directed language learning through ICT. The study's findings are useful for both learners and educators in leveraging ICT for self-directed language learning. Students must be equipped with ICT literacy and positive attitudes towards using ICT in English language learning activities. Teachers should also be equipped with ICT skills in order to provide learning experiences that are customized to students' needs and preferences in today's digital world. Furthermore, this study provides significant implications for educators and policy makers in providing ICT infrastructure that meets the students' needs.

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Arif, T. Z. Z. A., Sulistiyo, U., & Wachyunni, S. (2024). EFL university students’ self-directed language learning with ICT: a structural equation modelling approach*. Hachetetepé. Scientific Journal of Education and Communication, (29), 2205. https://doi.org/10.25267/Hachetetepe.2024.i29.2205

Author Biographies

Tubagus Zam Zam Al Arif, Universitas Jambi

He is a lecturer of English Language Education at the Department of Language and Literature, Teacher Training and Education Faculty, University of Jambi, Indonesia. His main interests are Teaching English as a Foreign Language (TEFL), ICT for ELT, and Technology-Enhanced Language Learning. He has written many articles in this related field. He has published many articles in this field which can feature on Scopus, WoS, Google Scholar, Sinta, Crossref, etc.

Urip Sulistiyo, Universitas Jambi

He is a professor of English Language Education at the Department of Language and Literature, Teacher Training and Education Faculty, University of Jambi, Indonesia. His main interests are Teaching English as a Foreign Language (TEFL), Curriculum, and Educational Policy. He has published several publications in this topic indexed in Scopus, WoS, Sinta, Crossref, etc. Similarly, as someone who has been teaching English for more than 25 years, he has conducted additional educational studies.

Sri Wachyunni, Universitas Jambi

She is a lecturer of English Language Education at the Department of Language and Literature, Teacher Training and Education Faculty, University of Jambi, Indonesia. Her main interests are Teaching English as a Foreign Language (TEFL), Teacher Identity, Teaching English for Young Learners, as well as cultural and gender studies. She has published many articles in this field which can feature on Scopus, WoS, Google Scholar, Sinta, Crossref, etc.

References

Alfadda, H. A., & Mahdi, H. S. (2021). Measuring Students’ Use of Zoom Application in Language Course Based on the Technology Acceptance Model (TAM). Journal of Psycholinguistic Research, 50(4), 883–900. https://doi.org/10.1007/s10936-020-09752-1

Arif, T. Z. Z. A, Sulistiyo, U., Ubaidillah, M. F., Handayani, R., Junining, E., & Yunus, M. (2022). A Look at Technology Use for English Language Learning from a Structural Equation Modeling Perspective. Computer Assisted Language Learning Electronic Journal, 23(2), 18–37.

Arif, T. Z. Z. A, Kurniawan, D., Handayani, R., Hidayati, & Armiwati. (2024). EFL university students’ acceptance and readiness for e-learning: a structural equation modeling approach. The Electronic Journal of E-Learning, 22(1), 1–16. www.ejel.org

Artman, B., & Crow, R. S. (2022). Instructional technology integration and self-directed learning: A dynamic duo for education. International Journal of Self-Directed Learning, 19(1), 30–44.

Bosch, C., Mentz, E., & Goede, R. (2019). Self-directed learning: A conceptual overview. In Self-Directed Learning for the 21st Century: Implications for Higher Education (pp. 1–36). AOSIS.

https://doi.org/https://doi.org/10.4102/aosis.2019.BK134.01

Chau, K. Y., Law, K. M. Y., & Tang, Y. M. (2021). Impact of Self-Directed Learning and Educational Technology Readiness on Synchronous E-Learning. Journal of Organizational and End User Computing, 33(6), 1–20. https://doi.org/10.4018/joeuc.20211101.oa26

Chin, W. W. (1998). The partial least squares approach for structural equation modeling. In In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295–336). Lawrence Erlbaum Associates Publishers.

Creswell, J.W., 2014. Research design: qualitative, quantitative, and mixed methods approaches. 4th edn. SAGE Publications, Inc.

Damrow, A. L., & Faye, T. P. El. (2022). I had to change: empowering students through self-study research. International Journal of Self-Directed Learning, 19(1), 17–29.

Dogham, R. S., Elcokany, N., Ghaly, A., Dawood, T. M. A., Aldakheel, F. M., Llaguno, M. B. B., & Mohsen, D. M. (2022). Self-directed learning readiness and online learning self-efficacy among undergraduate nursing students. International Journal of Africa Nursing Sciences, 17, 100490. https://doi.org/10.1016/J.IJANS.2022.100490

Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L.,

Buhalis, D., … Wright, R. (2023). Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on

opportunities, challenges and implications of generative conversational AI for research, practice and policy.

International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642

Fisher, M., King, J., & Tague, G. (2001). Development of a self-directed learning readiness scale for nursing education. Nurse Education Today, 21(7), 516–525. https://doi.org/10.1054/nedt.2001.0589

Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312

Garrison, D. R. (1997). Self-directed learning: Toward a comprehensive model. Adult Education Quarterly, 48(1), 18–33. https://doi.org/10.1177/074171369704800103

Hadiyanto, H., Failasofah, F., Armiwati, A., Abrar, M., & Thabran, Y. (2021). Students’ practices of 21st century skills between conventional learning and blended learning. Journal of University Teaching and Learning Practice, 18(3), 1–23. https://doi.org/10.53761/1.18.3.7

Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203

Hamidi, H., & Chavoshi, A. (2019). Social, individual, technological and pedagogical factors influencing mobile learning acceptance in higher education: A case from Iran. Telematics and Informatics, 38, 133–165. https://doi.org/10.1016/j.tele.2018.09.007

Kessler, G. (2018). Technology and the future of language teaching. Foreign Language Annals, 51(1). https://doi.org/10.1111/flan.12318

Kline, R. B. (2016). Principles and practices of structural equation modelling 4th edition. In Methodology in the social sciences.

Lai, Y., Saab, N., & Admiraal, W. (2022). University students’ use of mobile technology in self-directed language learning: Using the integrative model of behavior prediction. Computers and Education, 179, 104413. https://doi.org/10.1016/j.compedu.2021.104413

Marín-Díaz, V., & Sampedro, B. (2023). View of digital competence the university student. Hachetetepé. Revista Científica de Educación y Comunicación, 26. https://doi.org/10.25267/hachetetepe.2023.i26.1102

Mentz, E., & Bailey, R. (2019). A systematic review of research on the use of technology-supported cooperative learning to enhance self-directed learning. In Self-Directed Learning for the 21st Century: Implications for Higher

Education (1st ed., pp. 203–238). AOSIS. https://doi.org/https://doi.org/10.4102/aosis.2019.BK134.07

Mentz, E., Beer, J. De, & Bailey, R. (2019). Self-Directed Learning for the 21st Century: Implications for Higher Education (1st ed., Vol. 1). AOSIS. https://doi.org/10.4102/aosis.2019.bk134.01

Pan, X. (2020). Technology Acceptance, Technological Self-Efficacy, and Attitude Toward Technology-Based Self-Directed Learning: Learning Motivation as a Mediator. Frontiers in Psychology, 11, 1–11.

https://doi.org/10.3389/fpsyg.2020.564294

Pan, X., & Shao, H. (2020). Understanding factors influencing EFL students’ technology-based self-directed learning. International Journal of Social Sciences and Education Research, 6(4), 450–459. https://doi.org/10.24289/ijsser.781472

Park, K.-Y., Sung, T.-S., & Joo, C.-W. (2018). On the Relationship between College Students’ Attitude toward the Internet and their Self-directed English Learning Ability. Journal of The Korea Society of Computer and Information, 23(2), 117–123. https://doi.org/10.9708/jksci.2018.23.02.117

Payne, S. (2021). Using an e-portfolio system to evaluate student learning outcomes and to foster more self- direction within the curricula. International Journal of Self-Directed Learning, 18(1), 1–9.

Rahim, M. N., & Chandran, S. S. C. (2021). Investigating EFL Students’ Perceptions on E-learning Paradigm-Shift During Covid-19 Pandemic. Elsya : Journal of English Language Studies, 3(1), 56–66. https://doi.org/10.31849/elsya.v3i1.5949

Sulistiyo, U., Zam, T., Al Arif, Z., Handayani, R., Ubaidillah, M. F., & Wiryotinoyo, M. (2022). Determinants of Technology Acceptance Model (TAM) Towards ICT Use for English Language Learning. Journal of Language and Education, 8(2), 18-31 https://doi.org/10.17323/jle.2022.12467

Sumuer, E. (2018). Factors related to college students’ self-directed learning with technology. Australian Journal of Educational Technology, 34(4), 29–43.

Tekkol, I. A., & Demirel, M. (2018). An investigation of self-directed learning skills of undergraduate students. Frontiers in Psychology, 9(NOV), 1–14. https://doi.org/10.3389/fpsyg.2018.02324

Teo, T., Tan, Chee, S., Lee, C. B., Chai, C. S., Koh, J. H. L., Chen, W. L., & Horn Mun, C. (2010). The self-directed learning with technology scale (SDLTS) for young students: An initial development and validation. Computers and Education, 55(4), 1764–1771. https://doi.org/10.1016/j.compedu.2010.08.001

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.1006/mvre.1994.1019

Yan, Y., & Singh, M. (2023). On the Educational Theory and Application of Mobile-assisted Language Learning and Independent Learning in College English Teaching. Cilt, 29(3), 137–151.

Yavuzalp, N., & Bahcivan, E. (2021). A structural equation modeling analysis of relationships among university students’ readiness for e-learning, self-regulation skills, satisfaction, and academic achievement. Research and Practice in Technology Enhanced Learning, 16(1), 1–17. https://doi.org/10.1186/s41039-021-00162-y

Zhang, D., & Pérez-Paredes, P. (2019). Chinese postgraduate EFL learners’ self-directed use of mobile English learning resources. Computer Assisted Language Learning, 0(0), 1–26. https://doi.org/10.1080/09588221.2019.1662455

Zhou, L., Xue, S., & Li, R. (2022). Extending the Technology Acceptance Model to Explore Students’ Intention to Use an Online Education Platform at a University in China. SAGE Open, 12(1). https://doi.org/10.1177/21582440221085259

Zhu, M., & Bonk, C. J. (2019). Designing MOOCS to facilitate participant self-monitoring for self-directed learning. Online Learning Journal, 23(4), 106–134. https://doi.org/10.24059/olj.v23i4.2037