Exploring Factors Influencing Student Attitudes and Satisfaction with Mobile Translation Applications in Learning Chinese as a Foreign Language

Authors

DOI:

https://doi.org/10.60027/ijsasr.2024.4284

Keywords:

Learning Chinese as a Foreign Language (CFL); , Mobile Translation Applications; , Attitude; , Perceived Learning Outcomes; , Student Satisfaction

Abstract

Background and Aim: This study explored the factors that affected student attitudes regarding mobile translation applications and their satisfaction. The latent variables investigated in the study include effort expectancy (EE), performance expectancy (PE), attitude (AT), perceived learning outcomes (LO), facilitating conditions (FC), social influence (SI), and student satisfaction (SS). The objective of the research is to determine the extent to which each variable influences the use of mobile translation applications.

Materials and Methods: The research surveyed 520 international students on their opinions of utilizing mobile translation applications at two public universities in Liaoning Province, China. The data was analyzed via structural equation modeling (SEM) and confirmatory factor analysis (CFA).

Results: The results of the data analysis found that all the factors on attitude were significant, and all hypotheses were verified. Among them, student attitude had the greatest effect on perceived learning outcomes.

Conclusion: The findings underscored the importance of these factors in promoting the effective use of such applications, ultimately enhancing student satisfaction and perceived learning outcomes in the context of foreign language education.

References

Ajzen, I. (1980). Understanding attitudes and predicting social behavior. Englewood cliffs.

Alshammari, H. (2020). Chinese Language in Saudi Arabia: Challenges and Recommendations. English Language Teaching, 13(2), 75-85.

Alshare, K. A., & Lane, P. L. (2011). Predicting Student-Perceived Learning Outcomes and Satisfaction in ERP Courses: An Empirical Investigation. Communications of the Association for Information Systems, 28 (34), 571-584. https://doi.org/10.17705/1CAIS.02834

Ardies, J., De Maeyer, S., Gijbels, D., & van Keulen, H. (2015). Students’ attitudes toward technology. International Journal of Technology and Design Education, 25(1), 43-65.

Astin, A.W. (1997). What matters in college. JB.

Awang, Z. (2012). A Handbook on SEM Structural Equation Modelling: SEM Using AMOS Graphic. 5th edition. Kota Baru: Universiti Teknologi Mara Kelantan.

Bahri, H., & Mahadi, T.S.T. (2016). The Application of Mobile Devices in the Translation Classroom. Advances in language and literary studies, 7(6), 237-242.

Basith, A., Musyafak, N., Ichwanto, M.A., & Syahputra, A. (2019). Chinese Learning Anxiety on Foreign Students. European Journal of Educational Research, 8(4), 1193-1200.

Bentler, P.M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238–246. https://doi.org/10.1037/0033-2909.107.2.238

Botero, G.G., Questier, F., Cincinnato, S., He, T., & Zhu, C. (2019). Acceptance and usage of mobile-assisted language learning by higher education students. Journal of Computing in Higher Education, 30(3), 426-451.

Briz-Ponce, L., Pereira, A., Carvalho, L., Juanes-Méndez, J.A., & García-Peñalvo, F.J. (2017). Learning with mobile technologies – Students’ behavior. Computers in Human Behavior, 72, 612-620. doi:10.1016/j.chb.2016.05.027.

Brown, S.A., Massey, A.P., Montoya-Weiss, M.M., & Burkman, J.R. (2002). Do I really have to? User acceptance of mandated technology. European journal of information systems, 11(4), 283-295.

Crespo, M.Á.J. (2016). Mobile apps and translation crowdsourcing: The next frontier in the evolution of translation. Tradumàtica: traducció i tecnologies de la informació i la comunicació, (14), 75-84.

Dong, Y.R. (2013). Powerful learning tools for ELLs. The Science Teacher, 80(4), 51-57.

Dwivedi, Y.K., Rana, N.P., Jeyaraj, A., Clement, M., & Williams, M.D. (2019). Re-examining the unified theory of acceptance and use of technology (UTAUT): Toward a revised theoretical model. Information Systems Frontiers, 21, 719-734. https://doi.org/10.1007/s10796-017-9774-y

Eom, S.B., Wen, H.J., & Ashill, N. (2006). The determinants of students’ perceived learning outcomes and satisfaction in university online education: An empirical investigation. Decision Sciences Journal of Innovative Education, 4(2), 215-235.

Erasmus, E., Rothmann, S., & Van Eeden, C. (2015). A structural model of technology acceptance. SA Journal of Industrial Psychology, 41(1), 1-12. https://doi.org/10.4102/sajip.v41i1

Farley, H., Murphy, A., Johnson, C., Carter, B., Lane, M., Midgley, W., & Koronios, A. (2015). How Do Students Use Their Mobile Devices to Support Learning? A Case Study from an Australian Regional University. Journal of Interactive Media in Education, 14 (1), 1–13, DOI: http://dx.doi.org/10.5334/jime.ar

George, D., & Mallery, P. (2003). SPSS for Windows Step by Step: A Simple Guide and Reference. 11.0 Update. 4th edition. Boston: Allyn & Bacon.

Hao L, et al. (2017). SNAIL1 is essential for female gametogenesis in Arabidopsis thaliana. J Integr Plant Biol, 59(9), 629-641.https://doi.org/10.1111/jipb.12572

Hoi, V.N. (2020). Understanding higher education learners’ acceptance and use of mobile devices for language learning: A Rasch-based path modeling approach. Computers & Education, 146, 103761.

Ikhsan, R.B., Saraswati, L.A., Muchardie, B.G., & Susilo, A. (2019). The determinants of students’ perceived learning outcomes and satisfaction in BINUS online learning. In 2019 5th International Conference on New Media Studies (CONMEDIA) (pp. 68-73). IEEE.

Jacobsen, W. C., & Forste, R. (2011). The wired generation: Academic and social outcomes of electronic media use among university students. Cyberpsychology, Behavior, and Social Networking, 14(5), 275-280.

Kacetl, J., & Klímová, B. (2019). Use of smartphone applications in English language learning—A challenge for foreign language education. Education Sciences, 9(3), 179. https://doi.org/10.3390/educsci9030179

Knox, W.E. (1993). Does College Make a Difference? Long-Term Changes in Activities and Attitudes. Contributions to the Study of Education, Number 59. Greenwood Publishing Group, 88 Post Road West, Box 5007, Westport, CT 06881.

Koch, J., Salamonson, Y., Du, H.Y., Andrew, S., Frost, S.A., Dunncliff, K., & Davidson, P.M. (2011). Value of web-based learning activities for nursing students who speak English as a second language. Journal of Nursing Education, 50(7), 373-380.

Krejcie, R.V. & Morgan, D.W. (1970), Determining sample size for research activities. Educational and Psychological Measurement, 30(3),607-610

Lee, C.H., & Kalyuga, S. (2011). Effectiveness of different pinyin presentation formats in learning Chinese characters: A cognitive load perspective. Language Learning, 61(4), 1099-1118.

Lepp, A., Barkley, J.E., & Karpinski, A.C. (2015). The relationship between cell phone use and academic performance in a sample of US college students. Sage Open, 5(1), 2158244015573169.

Menezes, V. (2011). Affordances for language learning beyond the classroom. In Beyond the Language Classroom (pp. 59-71). London: Palgrave Macmillan UK.

Milošević, I., Živković, D., Manasijević, D., & Nikolić, D. (2015). The effects of the intended behavior of students in the use of M-learning. Computers in Human Behavior, 51, 207- 215.

Pedroso, R., Zanetello, L., Guimaraes, L., Pettenon, M., Goncalves, V., Scherer, J., Kessler, F., & Pechansky, F. (2016). Confirmatory factor analysis (CFA) of the crack use relapse scale (CURS). Archives of Clinical Psychiatry, 43 (3), 37-40.

Pynoo, B., Devolder, P., Voet, T., Vercruysse, J., Adang, L., & Duyck, P. (2007). Attitude as a measure for acceptance: Monitoring IS implementation in a hospital setting. SIGHCI 2007 Proceedings, 21.

Rai, N., & Thapa, B. (2015). A study on purposive sampling method in research. Kathmandu: Kathmandu School of Law, 5.

Sabah, N.M. (2016). Exploring students’ awareness and perceptions: Influencing factors and individual differences driving m-learning adoption. Computers in Human Behavior, 65, 522-533.

Sharma, G.P., Verma, R.C., & Pathare, P. (2005). Mathematical modeling of infrared radiation thin layer drying of onion slices. Journal of Food Engineering, 71(3), 282–286.

Sica, C., & Ghisi, M. (2007). The Italian versions of the Beck Anxiety Inventory and the Beck Depression Inventory-II: Psychometric properties and discriminant power. In M.A. Lange (Ed.), Leading-Edge Psychological Tests and Testing Research (pp.27-50). New York: Nova.

Venkatesh, V., Morris, M.G., Davis, G.B. and Davis, F.D. (2003) User Acceptance of Information Technology: Towards a Unified View. MIS Quarterly, 27, 425-478.

Yeap, J.A.L., Ramayah, T., & Soto-Acosta, P. (2016). Factors propelling the adoption of m- m-learning among students in higher education. Electronic Markets, 26(4), 323-338.

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Published

2024-06-21

How to Cite

Ma, X., & Li , C. (2024). Exploring Factors Influencing Student Attitudes and Satisfaction with Mobile Translation Applications in Learning Chinese as a Foreign Language. International Journal of Sociologies and Anthropologies Science Reviews, 4(3), 521–534. https://doi.org/10.60027/ijsasr.2024.4284

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