A review of AWE feedback: types, learning outcomes, and implications

Abstract

Automated writing evaluation (AWE) plays an important role in writing pedagogy and has received considerable research attention recently; however, few reviews have been conducted to systematically analyze the recent publications arising from the many studies in this area. The present review aims to provide a comprehensive analysis of the literature on AWE feedback for writing in terms of methodology, types of learners, types of feedback and its applications, learning outcomes, and implications. A total of 48 articles from Social Science Citation Index journals and four other important journals in the field of language education were collected and analyzed. The findings revealed that most previous studies on AWE applied quantitative research methods, rather than purely qualitative ones. The duration of the experiments in approximately 33% of the studies was shorter than ten weeks, and 10% of the studies were of one session only. The group size of over half of the studies had fewer than 30 participants, and 21% of the studies had medium to large group sizes (from 51 to 100). The focus of most of the articles was on L2 writers with little attention paid to L1 writers and K12 students. AWE feedback to some extent can improve students’ writing from the product-oriented aspect but is not as effective as human feedback (e.g. teacher or peer feedback). Students generally considered AWE feedback useful and were motivated when using it, although they noticed a lack of accuracy and explicitness as the feedback tended to be generic and formulaic. The results of the review have several implications for researchers, teachers, and developers of AWE systems.

Keywords:

• Automated feedback
• writing
• technology enhanced language learning
• systematic review

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research is financially supported by the Croucher Chinese Visitorships 2019-20 of the Croucher Foundation, Hong Kong SAR, the Research Cluster Fund (RG 78/2019-2020R) of The Education University of Hong Kong, and the Educational Science Development Program of Zhejiang Province (NO. 2020SCG030).

Notes on contributors

Qing-Ke Fu

Qing-Ke Fu is a lecture, his research interests include technology-assisted learning, game-based learning, Flipped classroom and STEM education.

Di Zou is the corresponding author of this paper. She is an Assistant Professor at the Education University of Hong Kong. Her research interests include technology-enhanced language learning, game-based language learning, and AI in English language education.

Haoran Xie is an Associate Professor at the Department of Computing and Decision Sciences, Lingnan University, Hong Kong. His research interests include artificial intelligence, big data, and educational technology. He is the Editor-in-Chief of Computers and Education: Artificial Intelligence.

Dr. Gary Cheng is an Associate Professor of the Department of Mathematics and Information Technology at The Education University of Hong Kong. With substantial years of work experience in Hong Kong academia, Dr. Cheng has built a wealth of knowledge and a network of support to unleash the potential of technology for teacher education.