A teaching experiment on a knowledge-network-based online translation learning platform

Abstract

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.