LAUSR

While translation competence assessment has been playing an increasingly facilitating role in translation teaching and learning, it still failed to offer fine-grained diagnostic feedback based on certain reliable translation competence standards. As such, this study attempted to investigate the feasibility of providing diagnostic information about students’ translation competence by integrating China’s Standards of English (CSE) with cognitive diagnostic assessment (CDA) approaches. Under the descriptive parameter framework of CSE translation scales, an attribute pool was established, from which seven attributes were identified based on students’ and experts’ think-aloud protocols. A checklist comprising 20 descriptors was developed from CSE translation scales, with which 458 students’ translation responses were rated by five experts. In addition, a Q-matrix was established by seven experts. By comparing the diagnostic performance of four widely-used cognitive diagnostic models (CDMs), linear logistic model (LLM) was selected as the optimal model to generate fine-grained information about students’ translation strengths and weaknesses. Furthermore, relationships among translation competence attributes were discovered and diagnostic results were shown to differ across high and low proficiency groups. The findings can provide insights for translation teaching, learning and assessment.