Revised 7 October 2022
Accepted 23 January 2023
Available Online 14 March 2023
College English teaching
Under the guidance of multi-modal discourse analysis theory, this article constructs the multi-modal teaching mode for College English at three stages: before class, during class, and after class. Non-English majors at two classes of A-level in differentiating instruction of Xi'an FanYi University are chosen as the research subjects. Among them, one is the Control Class in which the present teaching mode is applied, and the other is the Experimental Class in which the multi-modal teaching mode is applied. The experiment lasts four months. The scores of the final exam of College English in the third term are chosen as pre-test data and the scores in the fourth term as post-test data. The computer software SPSS20.0 is employed to conduct independent samples T-test and paired sample T-test. Meanwhile, students in the Experimental Class are required to finish a questionnaire at the end of the term and some students are chosen randomly to have an interview in order to testify that College English teaching mode based on multi-modal discourse analysis is better than the existing teaching mode. It can stimulate most students' interest in English and improve some students' abilities to have autonomic learning so that their English test scores are improved.
- © 2023 The Authors. Published by Athena International Publishing B.V.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/).
Cite This Article
TY - CONF AU - Xiaoru Gou PY - 2023 DA - 2023/03/14 TI - An Empirical Study of College English Teaching Reform From the Perspective of Multi-Modal Discourse Analysis: Taking Xi'an FanYi University as an Example BT - Proceedings of the 3rd International Conference on Education Studies: Experience and Innovation (ICESEI 2022) PB - Athena Publishing SP - 51 EP - 56 SN - 2949-8937 UR - https://doi.org/10.55060/s.atssh.230306.009 DO - https://doi.org/10.55060/s.atssh.230306.009 ID - Gou2023 ER -