Abstract
With the rapid development of Artificial Intelligence, automatic writing evaluation (AWE) has received much attention from English Foreign Language (EFL) writing teachers. However, the obstacles and potential problems of integrating AWE in EFL writing instruction have yet to be explored. Scholars have indicated that the effectiveness of AWE in EFL writing instruction depends on the learners' depth of reflection. Hence, this study proposes a learning approach that integrates AWE and peer assessment (PA) based on the knowledge-building theory, with the expectation that learners will be able to strengthen their reflections on AWE feedback through PA, and thereby improve their EFL writing performance. To examine the effectiveness of the proposed approach, a quasi-experiment was conducted in a university EFL writing class. One of the classes (33 students) was the experimental group using the PA-AWE approach, and the other class (31 students) was a control group that studied using the conventional AWE approach (C-AWE approach). Findings revealed that the PA-AWE group outperformed the C-AWE group regarding EFL writing performance, learning motivation, critical thinking, and reduced EFL writing anxiety. In addition, a thematic inductive qualitative analysis of the interview data indicated each approach's benefits and learning conceptions.
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Chen-Chen Liu, Shi-Jie Liu and Naini Wang. Project administration were performed by Gwo-Jen Hwang, Yun-Fang Tu and Youmei Wang. Methodology and supervision were performed Gwo-Jen Hwang and Yun-Fang Tu. The first draft of the manuscript was written by Chen-Chen Liu and Shi-Jie Liu. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Liu, CC., Liu, SJ., Hwang, GJ. et al. Engaging EFL students’ critical thinking tendency and in-depth reflection in technology-based writing contexts: A peer assessment-incorporated automatic evaluation approach. Educ Inf Technol 28, 13027–13052 (2023). https://doi.org/10.1007/s10639-023-11697-6
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DOI: https://doi.org/10.1007/s10639-023-11697-6