Grounded in the activity theory, we adopted a sequential explanatory mixed-methods approach to explore the impact of automated written corrective feedback (AWCF) on English as a foreign language (EFL) learners' academic writing skills (i.e. task achievement, coherence and cohesion, lexicon, and grammatical range and accuracy). To this end, two…

The present study aimed to examine and validate a rubric for translation quality assessment using Rasch analysis. To this end, the researchers interviewed 20 expert translation instructors to identify the factors they consider important for assessing the quality of students' translation. Based on the specific commonalities found throughout the…

Automated Writing Evaluation (AWE) systems have been found to enhance the accuracy, readability, and cohesion of writing responses (Stevenson & Phakiti, 2019). Previous research indicates that individual learners may have difficulty utilizing content-based AWE feedback and collaborative processing of feedback might help to cope with this…

This study investigates the potential for linguistic microfeatures related to length, complexity, cohesion, relevance, topic, and rhetorical style to predict L2 writing proficiency. Computational indices were calculated by two automated text analysis tools (Coh- Metrix and the Writing Assessment Tool) and used to predict human essay ratings in a…

Traditionally, at universities, English as a foreign language instructors have used a series of approaches to teach students how to write academic texts in English from both teacher preparation and regular programs. In spite of this, students continue to have problems writing the academic texts required of them in the different courses. Concerned…