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Direct linkQin, Ying – International Journal of Computer-Assisted Language Learning and Teaching, 2019
This study extracts the comments from a large scale of Chinese EFL learners' translation corpus to study the taxonomy of translation errors. Two unsupervised machine learning approaches are used to obtain the computational evidences of translation error taxonomy. After manually revision, ten types of English to Chinese (E2C) and eight types…
Descriptors: Taxonomy, Translation, Computer Assisted Instruction, Second Language Learning
Matthews, Joshua; O'Toole, John Mitchell; Chen, Shen – Computer Assisted Language Learning, 2017
This paper reports on task interaction, task success and word learning among second language (L2) learners of different levels of word recognition from speech (WRS) proficiency who used a CALL application previously shown to be effective in the development of L2 WRS. Participants (N = 65) were categorised into three levels of L2 WRS proficiency…
Descriptors: Word Recognition, Language Proficiency, Scores, Comparative Analysis
Liu, Jun; Sha, Sha; Zheng, Qinghua; Zhang, Wei – International Journal of Distance Education Technologies, 2012
Assigning difficulty level indicators to the knowledge units helps the learners plan their learning activities more efficiently. This paper focuses on how to use the topology of a knowledge map to compute and rank the difficulty levels of knowledge units. Firstly, the authors present the hierarchical structure and properties of the knowledge map.…
Descriptors: Foreign Countries, Knowledge Level, Difficulty Level, Educational Technology
