Computational Analysis of Lexical Features of Online and Offline Teaching Interactive Discourse
- DOI
- 10.2991/978-94-6463-230-9_84How to use a DOI?
- Keywords
- Lexical feature; online English teaching; offline English teaching; interactive discourse; computational linguistics
- Abstract
From the perspective of computational linguistics, this study establishes a corpus of online and offline interactive discourse, and studies the lexical features of interactive discourse in English teaching through computational methods, in order to improve the online education model and teaching interaction. It fins that the length, frequency of word and word clusters of interactive discourse are affected by the text type, network system and teaching mode. There is a certain correlation between online textual discourse and offline spoken interactive discourse, but their interaction efficiency is different. Based on the computational analysis, the research points out that the online teaching mode and the teaching interaction mode still need to be discussed, and the teaching interaction system needs to be further improved.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Jiaqi Liu PY - 2023 DA - 2023/09/04 TI - Computational Analysis of Lexical Features of Online and Offline Teaching Interactive Discourse BT - Proceedings of the 3rd International Conference on Internet, Education and Information Technology (IEIT 2023) PB - Atlantis Press SP - 701 EP - 707 SN - 2667-128X UR - https://doi.org/10.2991/978-94-6463-230-9_84 DO - 10.2991/978-94-6463-230-9_84 ID - Liu2023 ER -