Machine learning (ML) modelling techniques for mobile technology-integrated vocabulary learning on Chinese universities EFL students’ adoption

Abdul Rahim Hamdan

Abstract

Studying new vocabulary is an important goal for language students, as is making the learning of vocabulary a student-centered process. Of course, the goal of investigating what variables affect people’s willingness to embrace new technologies is to better adopt such technologies. The present study will thus look at the elements that have an impact on Chinese EFL college students’ adoption and usage of mobile technology-integrated vocabulary acquisition as a means to promote more learner-centric education. To better forecast human behavior, we successfully included the following elements from the technology adoption literature into the model: attitude toward change brought on by technology usage; attitude toward technology; desire; financial ramifications; aims; past behavior; perceived consistency; positive expected feelings; visibility; and so on. Furthermore, the characteristics, all of which are external factors that describe the traits of optimal technology design, are included in the development of Unified Theory of Acceptance and Use of Technology (UTAUT) using a machine-learning. All these elements help UTAUT progress. Among its many uses, ML-based modeling may be put to good use in enhancing pre-existing explanatory statistical models (UTAUT) by identifying and analyzing hidden patterns and correlations between their many aspects. This is possible because ML-based modeling may enhance traditional statistical explanations (UTAUT). © 2024 by author(s).

References

Hazaea AN, Alzubi A. Impact of mobile assisted language learning on learner autonomy in EFL reading context. Journal of Language and Education 2018; 4(2): 48–58. doi: 10.17323/2411-7390-2018-4-2-48-58

Andrews J, Higson H. Graduate employability, ‘soft skills’ versus ‘hard’ business knowledge: A European study. Higher Education in Europe 2008; 33(4): 411–422. doi: 10.1080/03797720802522627

Zheng C, Su Y, Lian J. Developing an online formative assessment system for a Chinese EFL course. In: Proceedings of the 22nd International Conference on Computers in Education; 30 November 2014–4 December 2014; Nara, Japan. pp. 532–535.

Bremner N. The multiple meanings of ‘student-centered’ or ‘learner-centred’ education, and the case for a more flexible approach to defining it. Comparative Education 2021; 57(2): 159–186. doi: 10.1080/03050068.2020.1805863

Venkatesh V, Thong JYL, Xu X. Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly 2012; 36(1): 157–178. doi: 10.2307/41410412

Lemon KN, White TB, Winer RS. Dynamic customer relationship management: Incorporating future considerations into the service retention decision. Journal of Marketing 2002; 66(1): 1–14. doi: 10.1509/jmkg.66.1.1.18447

Marangunić N, Granić A. Technology acceptance model: A literature review from 1986 to 2013. Universal Access in the Information Society 2015; 14(1): 81–95.

Holden RJ, Karsh BT. The technology acceptance model: Its past and its future in health care. Journal of Biomedical Informatics 2010; 43(1): 159–172. doi: 10.1016/j.jbi.2009.07.002

Chau PYK. An empirical assessment of a modified technology acceptance model. Journal of Management Information Systems 1996; 13(2): 185–204. doi: 10.1080/07421222.1996.11518128

Lee Y, Kozar KA, Larsen KR. The technology acceptance model: Past, present, and future. Communications of the Association for Information Systems 2003; 12(1): 50. doi: 10.17705/1CAIS.01250

Chen RJ. Investigating models for preservice teachers’ use of technology to support student-centered learning. Computers & Education 2010; 55(1): 32–42. doi: 10.1016/j.compedu.2009.11.015

Baddeley AD, Gathercole SE, Papagno C. The phonological loop as a language learning device. Psychological Review 1998; 105(1): 158–173. doi: 10.1037/0033-295x.105.1.158

Vernier S, Barbuzza S, Giusti SD, Moral GD. The five language skills in the EFL classroom. Nueva Revista de Lenguas Extranjeras 2008; 10(1): 263–291.

Henriksen B. Three dimensions of vocabulary development. Studies in Second Language Acquisition 1999; 21(2): 303–317.

Jang H. Supporting students’ motivation, engagement, and learning during an uninteresting activity. Journal of Educational Psychology 2008; 100(4): 798–811. doi: 10.1037/a0012841

Ryder JF, Tunmer WE, Greaney KT. Explicit instruction in phonemic awareness and phonemically based decoding skills as an intervention strategy for struggling readers in whole language classrooms. Reading and Writing 2008; 21(4): 349–369. doi: 10.1007/s11145-007-9080-z

Venkatesh V, Thong JY, Xu X. Student acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly 2012; 36(1): 157–178.

Tao H, Keshtegar B, Yaseen ZM. The feasibility of integrative radial basis M5Tree predictive model for river suspended sediment load simulation. Water Resources Management 2019; 33(13): 4471–4490.

Jingya B, Ye H, Jing W, et al. Quantitative analysis and comparison of BMI among Han, Tibetan, and Uygur university students in Northwest China. The Scientific World Journal 2013; 2013: 180863.

Tao H, Salih SQ, Saggi MK, et al. A newly developed integrative bio-inspired artificial intelligence model for wind speed prediction. IEEE Access 2020; 8: 83347–83358.