The short-term and long-term effects of the Automated Written Corrective Feedback (AWCF) on Chinese low-proficiency EFL students’ writing errors /

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Student Theses

2024

Cai, Bei

Thesis

Numerous studies have been conducted regarding the implementation of automated written corrective feedback (AWCF) in English writing instruction. While attempts have been made to assess the impact of AWCF based on students’ subsequent written work, comprehending how students internalize and utilize this feedback to minimize writing errors presents a substantial challenge, primarily due to the personal and private nature of this process. Concurrently, the findings related to the effects of AWCF on writing errors have been inconclusive and inconsistent due to the presence of conflicting results. Therefore, this study delves into exploring the influence of AWCF on the short-term and long-term retention of writing errors among Chinese low-proficiency EFL learners, employing error analysis alongside eye-tracking technology. A total of 118 English major students from a higher vocational college in China were recruited as participants for this research. These participants were randomly assigned to experimental and control groups. The experimental group engaged with the AWE system (Pigai) and received joint feedback (AWCF+teacher feedback), while the control group exclusively relied on teacher feedback. In addition to conventional writing tests, eye-tracking experiments were conducted separately before and after the writing instruction. Data collection included participants’ responses during the eye-tracking experiment, first-person eye movement video data, and corresponding gaze data. Leveraging the application of neural network technology in optical character recognition (OCR), combined with data from eye-tracking devices, we have developed a system that can transform first-person eye movement video data and gaze data into heatmaps and eye-tracking indices conducive to analysis. Various data analysis methods were employed, including neural network algorithms, heatmap analysis, Mann-Whitney U tests, and independent samples t-test. The results from the post-test writing errors, delayed eye-tracking experiment responses, heatmaps, and eye-tracking indices collectively highlight the benefits of using AWCF, which reduces students’ short-term and long-term writing errors, including mechanical errors, grammar errors, discourse errors, etc., facilitates error identification, and improves language proficiency while reducing processing time. The pedagogical implications were discussed

LG51.H43 Dr 2024eb Caib

https://educoll.lib.eduhk.hk/records/3SAWgBSK