English Translation Teaching Based on Digital Information Technology and Resources
DOI: 10.23977/trance.2024.060604 | Downloads: 26 | Views: 826
Author(s)
Chunlan Jiang 1, Ying He 1, Yu Zhang 1
Affiliation(s)
1 Center for Basic English Education Research, Xi'an Fanyi University, Xi'an, Shaanxi, 710105, China
Corresponding Author
Chunlan JiangABSTRACT
The main purpose of teaching translation is to promote students' English learning and cultivate English application ability through translation activities. As a kind of language teaching method, the development of translation activities should serve the teaching goals of the whole classroom, and combine with other teaching methods to jointly promote the improvement of students' English comprehension ability and application ability. The full use of digital information technology and digital resources in English translation teaching will greatly improve the effect of college English translation teaching. In order to deeply explore the role of digital information technology in English translation teaching, we have carried out research on the reform and exploration of English translation teaching based on digital information technology. This paper first discusses the teaching of English translation and its basic methods. This paper will combine the background of digital information teaching to analyze how to apply digital information technology and digital teaching resources in college English translation teaching, so as to improve the teaching effect of college English translation. Cultivate students' interest in learning and enhance students' translation ability. By using the image extraction method, the multimedia construction method and the classroom learning contrast method in the digital information technology, the sample is collected, the technical framework is analyzed, and the algorithm is simplified. And create a digital information technology teaching model. The experimental results of this paper show that the accuracy of dictionary translation is 44.5% by comparing the methods based on digital information technology and resources. The accuracy of the g and b translations is slightly lower than that of the dictionary method, with an accuracy of 40.5%. And the translation method based on digital information technology and resources has the highest accuracy rate of 79.8%. This essentially verifies the effectiveness and robustness of the proposed translation teaching method.
KEYWORDS
Digital Information Technology and Resources, English Translation Teaching, Teaching Reform, Image AlgorithmCITE THIS PAPER
Chunlan Jiang, Ying He, Yu Zhang, English Translation Teaching Based on Digital Information Technology and Resources. Transactions on Comparative Education (2024) Vol. 6: 30-39. DOI: http://dx.doi.org/10.23977/trance.2024.060604.
REFERENCES
[1] Amato G, Fa Lchi F, Vadicamo L. Image Retrieval. Multimedia Tools and Applications, 2016, 77(5):5385-5415.
[2] Kaljahi M A, Palaiahnakote S, Anisi M H, et al. Visual surveillance system. Multimedia Tools and Applications, 2019, 78(5):5791-5818.
[3] Ahmad J, Sajjad M, Rho S, et al. Multi-scale local structure patterns histogram for describing visual contents in social image retrieval systems. Multimedia Tools and Applications, 2016, 75(20):12669-12692.
[4] Thanh T M, Tanaka K. An image zero-watermarking algorithm based on the encryption of visual map feature with watermark information. Multimedia Tools and Applications, 2017, 76(11):13455-13471.
[5] Mbeudeu C D. Introducing translation-based activities in teaching English as a foreign language: A step towards the improvement of learners' accurate use of words and expressions in writing. Research in Pedagogy, 2017, 7(1):76-89.
[6] Kalmazova N. Teaching Law Students Pre-Translation Text Analysis. Studies in Logic Grammar & Rhetoric, 2016, 45(1):87-96.
[7] Rahman M M, Pandian A. A Critical Investigation of English Language Teaching in BangladeshUnfulfilled expectations after two decades of Communicative Language Teaching. English Today, 2018, 34(3):43-49.
[8] Ren Y, Liu F, Yan W, et al. A new visual evaluation criterion of visual cryptography scheme for character secret image. Multimedia Tools and Applications, 2019, 78(18):25299-25319.
[9] Zhang S, Zhang J, Guo P, et al. A FWCL-based method for visual vocabulary formation. Multimedia Tools & Applications, 2016, 75(1):647-665.
[10] Lakrissi Y, Saaidi A, Essahlaoui A. Novel dynamic color image watermarking based on DWT-SVD and the human visual system. Multimedia Tools & Applications, 2017, 77(1):1-25.
[11] John B A, Raj C, Sukumaran R, et al. Enhanced semantic visual secret sharing scheme for the secure image communication. Multimedia Tools and Applications, 2020, 79(23):17057-17079.
[12] X Wang, Pang Y, X Ma. Real distorted images quality assessment based on multi-layer visual perception mechanism and high-level semantics. Multimedia Tools and Applications, 2020, 79(35):25905-25920.
[13] Zhou Z, Zhang R, Zhu Z. Robust Kalman filtering with long short-term memory for image-based visual servo control. Multimedia Tools and Applications, 2019, 78(18):26341-26371.
[14] Chen T H, Lin K S, Lin C H. On the design of a two-decoding-option image secret sharing scheme. Multimedia Tools and Applications, 2018, 77(7):7865-7881.
[15] Shen B, Liu B D, Wang Q. Elastic net regularized dictionary learning for image classification. Multimedia Tools and Applications, 2016, 75(15):8861-8874.
[16] Wu X. AHP-BP-Based Algorithms for Teaching Quality Evaluation of Flipped English Classrooms in the Context of New Media Communication. International Journal of Information Technologies and Systems Approach, 2023, 16(2), 1-12.
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