Potential of Automated Writing Evaluation Feedback

This paper presents an empirical evaluation of automated writing evaluation (AWE) feedback used for L2 academic writing teaching and learning. It introduces the Intelligent Academic Discourse Evaluator (IADE), a new web-based AWE program that analyzes the introduction section to research articles and generates immediate, individualized, and discipline-specific feedback. The purpose of the study was to investigate the potential of IADE's feedback. A mixed-methods approach with a concurrent transformative strategy was employed. Quantitative data consisted of responses to Likert-scale, yes/no, and open-ended survey questions; automated and human scores for first and final drafts; and pre-/posttest scores. Qualitative data contained students' first and final drafts as well as transcripts of think-aloud protocols and Camtasia computer screen recordings, observations, and semistructured interviews. The findings indicate that IADE's colorcoded and numerical feedback possesses potential for facilitating language learning, a claim supported by evidence of focus on discourse form, noticing of negative evidence, improved rhetorical quality of writing, and increased learning gains.