Effects of Japanese University Students’ Characteristics on the Use of an Online English Course and TOEIC Scores
DOI:
https://doi.org/10.1558/cj.36748Keywords:
ICT, CALL, self-regulated learning, out-of-class online language learning, EFL, Individual differencesAbstract
The effective use of Information and Communication Technology (ICT) can have positive effects on the development of learners’ English abilities. To what degree it is effective is partly determined by learners’ characteristics in ICT use. However, these characteristics have not yet been sufficiently discussed in Japan. This study, then, explored how the characteristics of Japanese EFL university students related to their actual use of an online English course and whether it led to the development of their English abilities. In the survey, 130 Japanese university students were asked to self-evaluate their attitudes toward computer-assisted language learning (CALL) and the use of technology in an out-of-classroom situation. As a result, it became clear that most of the students were not confident in using the technology and did not use it actively outside the classroom. Cluster analysis was employed with a focus on individual differences, revealing that the time students actually spent on the course and their high evaluations of the effectiveness of CALL did not necessarily predict development of English abilities. It was suggested that individual differences should be carefully considered in adopting online English courses effectively in higher education institutions.
References
Academic eXchange for Information Environment and Strategy (AXIES). (2016). Koto kyoiku kikan ni okeru ICT no rikatsuyo ni kansuru tyousa kenkyu [A survey on the use of ICT in Japanese higher institutions]. Retrieved from https://axies.jp/ja/ict/2015report.pdf/
Celik, V., & Yesilyurt, E. (2013). Attitudes to technology, perceived computer self-efficacy and computer anxiety as predictors of computer supported education. Computers & Education, 60, 148–158. https://doi.org/10.1016/j.compedu.2012.06.008
Chang, C., Hajiyev, J., & Su, C. (2017). Examining the students’ behavioral intention to use e-learning in Azerbaijan? The general extended technology acceptance model for e-learning approach. Computers & Education, 111, 128–143. https://doi.org/10.1016/j.compedu.2017.04.010
Csizér, K., & Dörnyei, Z. (2005). Language learners’ motivational profiles and their motivated learning behavior. Language Learning, 55(4), 613–659. https://doi.org/10.1111/j.0023-8333.2005.00319.x
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982
Dörnyei, Z. (2003). Questionnaires in second language research: Construction, administration, and processing. Mahwah, NJ: Lawrence Erlbaum Associates.
Grgurović, M., Chapelle, C. A., & Shelley, M. C. (2013). A meta-analysis of effectiveness studies on computer technology-supported language learning. ReCALL, 25(2), 165–198. https://doi.org/10.1017/s0958344013000013
Hsu, L. (2016). An empirical examination of EFL learners’ perceptual learning styles and acceptance of ASR-based computer-assisted pronunciation training. Computer Assisted Language Learning, 29(5), 881–900. https://doi.org/10.1080/09588221.2015.1069747
Kawaguchi, Y. (2015). Seisa ga computer shien gogaku gakusyu taido ni oyobosu eikyo: chugaku, koko, daigakusei wo taisyou to shite [The effects of gender differences on computer-assisted language learning attitude: Focusing on junior high school, high school, and university students]. Evergreen, 9, 9–16.
Kawaguchi, Y., & Kusanagi, K. (2016). Nihonjin eigo gakusyusya wo taisyo to shita atarashii computer shien gogaku gakusyu taido syakudo no sakusei [Developing a new scale for computer assisted language learning attitudes focusing on Japanese EFL learners]. Language Education & Technology, 52, 257–277.
Kizilcec, R. F., Pérez-Sanagustín, M., & Maldonado, J. J. (2017). Self-regulated learning strategies predict learner behavior and goal attainment in massive open online courses. Computers & Education, 104, 18–33. https://doi.org/10.1016/j.compedu.2016.10.001
Kunnan, A. J. 1998. An introduction to structural equation modelling for language assessment research. Language Testing, 15, 295–332. https://doi.org/10.1177/026553229801500302
Lai, C. (2013). A framework for developing self-directed technology use for language learning. Language Learning & Technology, 17(2), 100–122. Retrieved from https://www.lltjournal.org/item/2812
Lai, C., & Gu, M. (2011). Self-regulated out-of-class language learning with technology. Computer Assisted Language Learning, 24(4), 317–335. https://doi.org/10.1080/09588221.2011.568417
Lai, C., Zhu, W. H., & Gong, G. (2015) Understanding the quality of out-of-class English learning. TESOL Quarterly, 49(2), 278–308. https://doi.org/10.1002/tesq.171
Lee, J., & Choi, H. (2017). What affects learner’s higher-order thinking in technology-enhanced learning environments? The effects of learner factors. Computers & Education, 115, 143–152. https://doi.org/10.1016/j.compedu.2017.06.015
Lee, C., Yeung, A. S., & Ip, T. (2017). University English language learners’ readiness to use computer technology for self-directed learning. System, 67, 99–110. https://doi.org/10.1016/j.system.2017.05.001
Lin, H. (2015). A meta-synthesis of empirical research on the effectiveness of computer-mediated communication (CMC) in SLA. Language Learning & Technology, 19(2), 85–117. http://dx.doi.org/10125/44419
Liu, M., Moore, Z., Graham, L., & Lee, S. (2002). A look at the research on computer-based technology use in second language learning. Journal of Research on Technology in Education, 34(3), 250–273. https://doi.org/10.1080/15391523.2002.10782348
Mahmoodi, M. H., Kalantari, B., & Ghaslani, R. (2014). Self-regulated (SRL), motivation and language achievement of Iranian EFL learners. Prosodia-Social and Behavioral Sciences, 98, 1062–1068. https://doi.org/10.1016/j.sbspro.2014.03.517
Richards, J. C., & Schmidt, R. (Eds.). (2010). Longman dictionary of language teaching and applied linguistics (4th ed.). London, England: Pearson.
Sánchez, R. A., & Hueros, A. D. (2010). Motivational factors that influence the acceptance of Moodle using TAM. Computers in Human Behavior, 26, 1632–1640. https://doi.org/10.1016/j.chb.2010.06.011
Seaman, J. E., Allen, I. E., & Seaman, J. (2018). Grade increase: Tracking distance education in the United States. Retrieved from https://onlinelearningsurvey.com/reports/gradeincrease.pdf
Tabak, F. & Nguyen, N. T. (2013). Technology acceptance and performance in online learning environments: Impact of self-regulation. MERLOT Journal of Online Learning and Teaching, 9(1), 116–130. Retrieved from http://jolt.merlot.org/vol9no1/tabak_0313.pdf
Tsai, Yea-Ru. (2015). Applying the technology acceptance model (TAM) to explore the effects of a course management system (CMS)-assisted EFL writing instruction. CALICO Journal, 32(1), 153–171. https://doi.org/10.1558/calico.v32i1.25961
U. S. Department of Education. (2010). Evaluation of evidence-based practices in online learning: A meta-analysis and review of online learning studies. Retrieved from https://www2.ed.gov/rschstat/eval/tech/evidence-based-practices/finalreport.pdf
Van Aacken, S. (1999). What motivates L2 learners in acquisition of kanji using CALL: A case study. Computer Assisted Language Learning, 12, 113–136. https://doi.org/10.1076/call.12.2.113.5723
Wang, C., Shannon, D. M., & Ross, M. E. (2013). Students’ characteristics, self-regulated learning, technology self-efficacy, and course outcomes in online learning. Distance Education, 34(3), 302–323. https://doi.org/10.1080/01587919.2013.835779
Yamamori, K., Isoda, T., Hiromori, T., & Oxford, R. L. (2003). Using cluster analysis to uncover L2 learner differences in strategy use, will to learn, and achievement over time. International Review of Applied Linguistics in Language Teaching, 41(4), 381–409. https://doi.org/10.1515/iral.2003.017
