Optimization of College English Dynamic Multimodal Model Teaching Based on Deep Learning

نوع مقاله : مقاله مروری - تحلیلی

نویسندگان
Yanli Zhao 1
Nur Ainil Binti Sulaiman 2
1 Faculty of Education, Universiti Kebangsaan Malaysia, 43600 UKM Bangi Selangor, Malaysia
2 Foreign Languages Department, Zhengzhou Business University, Zhengzhou Gongyi, 451200, China
10.48311/LRR.14.5.285
چکیده
Since 2010, deep learning has been further developed, and the concept of multi-modality has penetrated into all walks of life. However, it has not been fully researched and applied in college English teaching, so this study modeled and practiced the multimodal teaching method of college English under the deep learning mode and its application. The definitions of modality and medium are first introduced, and then the definition of multimodality in this study is clarified. Then the classification of multimodal transport is expounded. The random forest algorithm is chosen as the main algorithm of this research, and a dynamic multimodal model is established. After that, there was a collaboration with a university and sophomore students were selected for practice. After processing and analyzing the collected data, it was found that in the data sample of 268 students, the number of students who did not study independently accounted for 24%, which indicates that most college students lack interest in learning English. Preliminary tests were also conducted on students' English proficiency throughout the year, and the results showed that the students' English proficiency was at a pass level and the overall English proficiency was weak. Reassessment of students' English proficiency showed that the actual teaching effect of each English proficiency was greater than 85%, and the effectiveness of English teaching in the selected universities was significantly improved. The average score improved by 8 points, indicating that multimodal teaching is scientifically effective

After a semester of multimodal teaching, the English teaching effectiveness of the university selected in this article has significantly improved. The research results indicate that the development of deep computer learning has introduced multimodal concepts into the teaching field, which is very suitable for assisting language learning based on its own advantages.

After a semester of multimodal teaching, the English teaching effectiveness of the university selected in this article has significantly improved. The research results indicate that the development of deep computer learning has introduced multimodal concepts into the teaching field, which is very suitable for assisting language learning based on its own advantages.

کلیدواژه‌ها

موضوعات

Abdel, R. A. (2021). The multimodal recycling machine: toward a cognitive-pragmatic theory of the text/image production. Journal of Graphic Novels and Comics, 12(3), 207-239. https://doi.org/10.1080/21504857.2019.1651358
Bartlett, P. L., Montanari, A., & Rakhlin, A. (2021). Deep learning: a statistical viewpoint. Acta numerica, 30, 87-201. https://doi.org/10.1017/S0962492921000027
Bashar, A. (2019). Survey on evolving deep learning neural network architectures. Journal of Artificial Intelligence, 1(02), 73-82. https://doi.org/10.36548/jaicn.2019.2.003
Chen, Q., & Jiang, G. (2018). Why are you amused: Unveiling multimodal humor from the prototype theoretical perspective. The European Journal of Humour Research, 6(1): 62-84. https://doi.org/10.7592/EJHR2018.6.1.chen
Dargan, S., Kumar, M., Ayyagari, M. R., & Kumar, G. (2020). A survey of deep learning and its applications: a new paradigm to machine learning. Archives of Computational Methods in Engineering, 27, 1071-1092. https://doi.org/10.1007/s11831-019-09344-w
Delecraz, S., Becerra-Bonache, L., Favre, B., Nasr, A., & Bechet, F. (2021). Multimodal machine learning for natural language processing: disambiguating prepositional phrase attachments with images. Neural Processing Letters, 53(5), 3095-3121. https://doi.org/10.1007/s11063-020-10314-8
Derakhshan, A., Wang, Y. L., Wang, Y. X., & Ortega-Martín, J. L. (2023). Towards Innovative Research Approaches to Investigating the Role of Emotional Variables in Promoting Language Teachers’ and Learners’ Mental Health. International Journal of Mental Health Promotion, 25 (7): 1-10. doi:10.32604/ijmhp.2023.029877.

Diao, L., & Hu, P. (2021). Deep learning and multimodal target recognition of complex and ambiguous words in automated English learning system. Journal of Intelligent & Fuzzy Systems, 40(4), 7147-7158. https://doi.org/10.3233/JIFS-189543
Dong, S. (2020). Intelligent English teaching prediction system based on SVM and heterogeneous multimodal target recognition. Journal of Intelligent & Fuzzy Systems, 38(6), 7145-7154. https://doi.org/10.3233/JIFS-179792
Fu, J., & Wang, Y. (2022). Inspecting EFL Teachers’ Academic Literacy Development in Multilingual Contexts: A Global Vision. Heliyon, e12143(8):1-6. doi: http://doi.org/10.1016/j.heliyon.2022.e12143.
Gao, Y., Zeng, G. Wang, Y., Klan, A. & Wang, X. (2022). Exploring educational planning, teacher beliefs, and teacher practices during the pandemic: A study of science and technology-based universities in China. Frontiers in Psychology, 13, 903244. https://doi.org/10.3389/fpsyg.2022.903244.
Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31(3), 685-695. https://doi.org/10.1007/s12525-021-00475-2
Lestari, A. S. (2022). Digital Based Learning Management With A Multimodal Approarch. Al-Tanzim: Jurnal Manajemen Pendidikan Islam, 6(2), 465-476. https://doi.org/10.33650/al-tanzim.v6i2.3235
Li, M., Liu, M., Jiang, Z., Zhao, Z., Zhang, J., Ge, M., ... & Wang, Y. (2022). Multimodal Emotion Recognition and State Analysis of Classroom Video and Audio Based on Deep Neural Network. Journal of Interconnection Networks, 22(Supp04), 2146011. https://doi.org/10.1142/S0219265921460117
Macagno, F., & Pinto, R. B. W. S. (2020). Reconstructing Multimodal Arguments in Advertisements: Combining Pragmatics and Argumentation Theory. In Argumentation Through Languages and Cultures (pp. 141-176). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-19321-7_7

Marufuzzaman, M., & Ekşioğlu, S. D. (2017). Designing a reliable and dynamic multimodal transportation network for biofuel supply chains. Transportation Science, 51(2): 494-517. https://doi.org/10.1287/trsc.2015.0632
Mater, A. C., & Coote, M. L. (2019). Deep learning in chemistry. Journal of chemical information and modeling, 59(6), 2545-2559. https://doi.org/10.1021/acs.jcim.9b00266
Nemati, S., Rohani, R., Basiri, M. E., Abdar, M., Yen, N. Y., & Makarenkov, V. (2019). A hybrid latent space data fusion method for multimodal emotion recognition. IEEE Access, 7, 172948-172964. https://doi.org/10.1109/ACCESS.2019.2955637
Pan, X., & Zhang, Z. (2020). An empirical study of application of multimodal approach to teaching reading in EFL in senior high school. International Journal of Emerging Technologies in Learning (iJET), 15(2), 98-111. https:/doi.org/10.3991/ijet.v15i02.11267
Ramtohul, P., Engelbert, M., Malclès, A., Gigon, E., Miserocchi, E., Modorati, G., ... & Freund, K. B. (2021). Bacillary layer detachment: multimodal imaging and histologic evidence of a novel optical coherence tomography terminology: literature review and proposed theory. Retina, 41(11), 2193-2207. https://doi.org/10.1097/IAE.0000000000003217
Stöckl, H. (2019). Linguistic multimodality–multimodal linguistics: a state-of-the-art sketch. Multimodality: disciplinary thoughts and the challenge of diversity. de Gruyter, Berlin/Boston, 41-68. https://doi.org/10.1080/21504857.2019.1651358
Wang, J., Cheng, R., & Liao, P. C. (2021). Trends of multimodal neural engineering study: a bibliometric review. Archives of Computational Methods in Engineering, 28(7): 4487-4501. https://doi.org/10.1007/s11831-021-09557-y
Wang, Y. (2022). Research on the method of educational text classification based on deep learning. International Journal of Continuing Engineering Education and Life Long Learning, 32(3), 313-326. https://doi.org/10.1504/IJCEELL.2022.124032
Wang, Y. (2017). Construction elements and path of practical education model in universities. EURASIA Journal of Mathematics, Science and Technology, 13(10): 6775-6782. https://doi.org/10.12973/ejmste/78525.
Wang, Y., & Hemchua, S. (2022). Can we learn about culture by EFL textbook images?: A semiotic approach perspective. Language Related Research, 13(3), 479-499. https://doi.org/10.29252/LRR.13.3.18.
Wang, Y. (2023). Probing into the boredom of online instruction among Chinese English language teachers during the Covid-19 pandemic. Current Psychology, 43(1):1-15.https://doi.org/10.1007/s12144-022-04223-3.
Wang, Y., Pan, Z. W., & Wang, M. Z. (2023). The moderating effect of participation in online learning on EFL teachers’ teaching ability. Heliyon, 9(3)e13890: 1-12. doi: https:// doi.org/10.1016/j.heliyon.2023.e13890.
Wang, Y., Derakhshan, A., Pan, Z., & Ghiasvand, F. (2023). Chinese EFL Teachers’ Writing Assessment Feedback Literacy: A Scale Development and Validation Study. Assessing Writing, 56: 1-16. doi: https://doi.org/10.1016/j.asw.2023.100726.
جستارهای زبانی
دوره 14، شماره 5 - شماره پیاپی 77
آذر و دی 1402
صفحه 285-307