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Scaling Students' Self-Efficacy on Machine Translation Post-Editing: Psychometric Properties of the Scale and Their Associations
Article describes how Machine Translation Post-Editing has emerged as a productivity-enhancing practice in the language service industry, where human editors correct the output of machine translation systems. this research paper aims to assess students' self-efficacy in translation learning, specifically in the context of MTPE, and explore the factor structure, psychometric properties, and internal associations of their self-efficacy.
Date:
November 1, 2023
Creator:
Li, Qing & Huang, Tai-yi
System:
The UNT Digital Library