A Teaching Experiment on a Knowledge-Network-Based Online Translation Learning Platform

  • Yuanyuan Mu
  • Wenting YangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11984)


This paper aims to elaborate on the design and application of an online platform as a knowledge-network-based system for online teaching/(self-)learning of translation in/between English and Chinese. We have two purposes for this research: first, to obtain a good understanding of translation trainees’ learning behaviors in the corpus-assisted and knowledge-network-based translation learning setting, in the hope that sufficient data will be collected to draw a model of knowledge-network-based learning. Equally important is our second purpose, which is to initiate a more systematic and in-depth data-based empirical investigation into teaching designs for knowledge-based translation learning. This research conducts an experiment on how teachers can use knowledge nodes to organize online translation learning and how students perceive knowledge-network-based learning. The experiment reveals a rising trend of students’ translation quality and they generally hold a positive attitude towards this learning model. Based on theoretical discussions of the platform design rationale and the findings from the teaching experiment, this paper explores how the knowledge-network-based translation learning can assist students in forming more efficient translation learning strategies.


Knowledge network Online translation learning Corpus-based translation teaching 



The work was substantially supported by The National Social Science Fund of China (Project No. 19BYY125).


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Foreign LanguagesAnqing Normal UniversityAnqingChina
  2. 2.Center for Translation Studies and Specialized CorporaHefei University of TechnologyHefeiChina

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