Abstract
Recognition technologies had been prevailing and widely used for EFL learning. We investigated the different recognitions used for EFL writing based on image-to-text, translated speech-to-text, and location-to-text recognitions – ITR, TSTR, and LTR. A quasi-experiment was implemented for 12 weeks in a vocational high school with experimental and control groups in two stages. Pre-test, posttests 1 and 2, questionnaires, and interviews were conducted and analyzed. Experimental learners, who wrote writing based on ITR and TSTR, outperformed control learners who wrote that based on TSTR only. Also, the experimental learners, who wore writing based on ITR, TSTR, and LTR, outperformed the control learners who wrote that based on ITR and TSTR. Particularly, LTR was beneficial for identifying controlling ideas and addressing the writing topics. ITR was beneficial for brainstorming and generating more ideas. TSTR was beneficial for yielding and transferring writing contents into words. The multiple recognitions were beneficial for most EFL writers, especially for low-ability language writers. Most writers were interested in describing based on authentic context learning. However, they complained about the low accuracy of LTR and TSTR and the difficulty of ITR texts when writing. Accordingly, the LTR database with various categories of places, the generation of ITR based on the language abilities of learners, and the higher accuracy of TSTR should be strictly considered when applying multiple recognitions for EFL writing.
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References
Davis, F.D., Bagozzi, R.P., Warshaw, P.R.: User acceptance of computer technology: a comparison of two theoretical models. Manag. Sci. 35(8), 982–1003 (1989)
Dong, G., Wang, N., Kuang, G.: Sparse representation of monogenic signal: with application to target recognition in SAR images. IEEE Sig. Process. Lett. 21(8), 952–956 (2014)
Fu, Q.K., Lin, C.J., Hwang, G.J., Zhang, L.: Impacts of a mind mapping-based contextual gaming approach on EFL students’ writing performance, learning perceptions and generative uses in an English course. Comput. Educ. 137, 59–77 (2019)
Godwin-Jones, R.: Emerging technologies: Mobile apps for language learning. Lang. Learn. Technol. 15(2), 2–11 (2011)
Haug, K.N., Klein, P.D.: The effect of speech-to-text technology on learning a writing strategy. Read. Writ. Q. 34(1), 47–62 (2018)
Huang, Y.M., Shadiev, R., Hwang, W.Y.: Investigating the effectiveness of speech-to-text recognition applications on learning performance and cognitive load. Comput. Educ. 101, 15–28 (2016)
Hwang, W.Y., Chen, H.S.L., Shadiev, R., Huang, R.Y.M., Chen, C.Y.: Improving English as a foreign language writing in elementary schools using mobile devices in familiar situational contexts. Comput. Assist. Lang. Learn. 27(5), 359–378 (2014)
Hwang, W.Y., Li, Y.H., Shadiev, R.: Exploring effects of discussion on visual attention, learning performance, and perceptions of students learning with STR-support. Comput. Educ. 116, 225–236 (2018)
Hwang, W.Y., Manabe, K., Cai, D.J., Ma, Z.H.: Collaborative kinesthetic English learning with recognition technology. J. Educ. Comput. Res. 58(5), 946–977 (2020)
Kotseas, E.: Asian students’ challenges in writing with confidence. NYS TESOL J. 4(2), 78–85 (2017)
Kuo, T.C.T., Shadiev, R., Hwang, W.Y., Chen, N.S.: Effects of applying STR for group learning activities on learning performance in a synchronous cyber classroom. Comput. Educ. 58(1), 600–608 (2012)
Lan, Y.J.: Contextual EFL learning in a 3D virtual environment. Lang. Learn. Technol. 19(2), 16–31 (2015)
Lee, S.M., Briggs, N.: Effects of using machine translation to mediate the revision process of Korean university students’ academic writing. ReCALL 1–16 (2020)
Li, Z., Hegelheimer, V.: Mobile-assisted grammar exercises: effects on self-editing in L2 writing. Lang. Learn. Technol. 17(3), 135–156 (2013)
Liu, Y., Jang, B.G., Roy-Campbell, Z.: Optimum input mode in the modality and redundancy principles for university ESL students’ multimedia learning. Comput. Educ. 127(August), 190–200 (2018)
Matthews, J., O’Toole, J.M., Chen, S.: The impact of word recognition from speech (WRS) proficiency level on interaction, task success and word learning: design implications for CALL to develop L2 WRS. Comput. Assist. Lang. Learn. 30(1–2), 22–43 (2017)
Mayer, R.E.: Cognitive theory of multimedia learning. In: The Cambridge Handbook of Multimedia Learning, 2nd edn., pp. 43–71 (2005)
Neviarouskaya, A., Prendinger, H., Ishizuka, M.: Affect analysis model: novel rule-based approach to affect sensing from text. Nat. Lang. Eng. 17(1), 95–135 (2011)
Nguyen, T.H., Hwang, W.Y., Pham, X.L., Ma, Z.H.: User-oriented EFL speaking through application and exercise: instant speech translation and shadowing in authentic context. Educ. Technol. Soc. 21(4), 129–142 (2018)
Nguyen, T.H., Hwang, W.Y., Pham, X.L., Pham, T.: Self-experienced storytelling in an authentic context to facilitate EFL writing. Comput. Assist. Lang. Learn. 1–30 (2020)
Shadiev, R., Huang, Y.-M., Hwang, J.-P.: Investigating the effectiveness of speech-to-text recognition applications on learning performance, attention, and meditation. Educ. Tech. Res. Dev. 65(5), 1239–1261 (2017). https://doi.org/10.1007/s11423-017-9516-3
Shadiev, R., Hwang, W.Y., Chen, N.S., Huang, Y.M.: Review of speech-to-text recognition technology for enhancing learning. Educ. Technol. Soc. 17(4), 65–84 (2014)
Shadiev, R., Wu, T.T., Huang, Y.M.: Using image-to-text recognition technology to facilitate vocabulary acquisition in authentic contexts. ReCALL 32(2), 195–212 (2020)
Shadiev, R., Zhang, Z.H., Wu, T.T., Huang, Y.M.: Review of studies on recognition technologies and their applications used to assist learning and instruction. Educ. Technol. Soc. 23(4), 59–74 (2020)
Shang, H.F.: Exploring online peer feedback and automated corrective feedback on EFL writing performance. Interact. Learn. Environ. 1–13 (2019)
Spector, J.M., Merrill, M.D.: Handbook of research on educational communications and technology. Br. J. Educ. Technol. 35(4) (2004)
Sun, J.C., Chang, K., Chen, Y.: GPS sensor-based mobile learning for English: an exploratory study on self- efficacy, self-regulation and student achievement. Res. Pract. Technol. Enhanc. Learn. 10, 1–8 (2015)
Wang, F., Hwang, W.Y., Li, Y.H., Chen, P.T., Manabe, K.: Collaborative kinesthetic EFL learning with collaborative total physical response. Comput. Assist. Lang. Learn. 32(7), 745–783 (2019)
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Hwang, WY., Nguyen, VG., Chin, CC., Purba, S.W.D., Ghinea, G. (2022). Investigation of Multiple Recognitions Used for EFL Writing in Authentic Contexts. In: Huang, YM., Cheng, SC., Barroso, J., Sandnes, F.E. (eds) Innovative Technologies and Learning. ICITL 2022. Lecture Notes in Computer Science, vol 13449. Springer, Cham. https://doi.org/10.1007/978-3-031-15273-3_48
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