Scaling Students’ Self-Efficacy on Machine Translation Post-Editing: Psychometric Properties of the Scale and Their Associations

Year 2023, Volume: 10 Issue: 6, 229 - 244, 01.11.2023

Qing Li , Tai-yi Huang

https://doi.org/10.17275/per.23.98.10.6

Abstract

Machine Translation Post-Editing (MTPE) has emerged as a productivity-enhancing practice in the language service industry, where human editors correct the output of machine translation systems. To ensure that students of translation possess the necessary skills for MTPE, it is essential to understand their self-efficacy in this domain. 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. The study utilized a modified survey adapted from the Scale for Assessing Translators' Self-Efficacy and collected responses from 65 undergraduate students in a Chinese university. The survey data underwent reliability and validity analyses, including exploratory factor analysis, to assess the measurement tool's consistency, stability, and construct validity. The results indicated a high reliability of the scale (Cronbach's Alpha = 0.914) and revealed three primary dimensions of self-efficacy: Decision-making of MTPE, Communicative Competence of MTPE, and Strategic Competence of MTPE, and the strong inter-correlations suggests that they collectively measure the construct of translators' self-efficacy of MTPE, providing insights into the skills and abilities required for effective MTPE. The findings contribute to the development of psychometric tools for further research in translation and promote pedagogical reform to align with evolving market trends emphasizing human-machine collaborative translation.

Keywords

self-efficacy, machine translation post-editing, psychometric investigation

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