How effectively can EFL students use automated written corrective feedback (AWCF) in research writing?

Guo, Qian; Feng, Ruiling; Hua, Yuanfang

doi:10.1080/09588221.2021.1879161

Cited by 55 publications

(44 citation statements)

References 42 publications

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“…However, the accuracy of low and high proficiency learners did not show a significant change. The pedagogical benefits of adopting Grammarly are confirmed in this study, which lends further support to Guo et al [11]'s finding concerning the positive role played by Grammarly in reducing L2 learners' written error. Besides, the result concerning low proficiency learners is in accordance with the argument of Lin and Griffith [19] that students with lower L2 proficiency are unable to use ACF to generate correct grammatical forms because they lack a comprehensive understanding of the target structure.…”

Section: Discussionsupporting

confidence: 89%

Understanding the Effects of Automated Writing Corrective Feedback on L2 Writing Accuracy Across Proficiency Levels

2021

FER

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Exploring the effects of automated writing evaluation (AWE) is essential for maximizing the potential of using technology for language learning. Notwithstanding the large amount of research centered on AWE, the differential effects of automated written corrective feedback (AWCF) on writing accuracy across various proficiency levels remain to be empirically clarified. This current study fills the void in the existing literature by taking a quantitative approach in exploring the efficacy of Grammarly on L2 writing accuracy with various language proficiency.

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Section: Discussionsupporting

confidence: 89%

Understanding the Effects of Automated Writing Corrective Feedback on L2 Writing Accuracy Across Proficiency Levels

2021

FER

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“…Independent studies on Grammarly, on the other hand, have only recently emerged (Barrot, 2020;Gao & Ma, 2020;Guo et al, 2021;Link et al, 2020;Waer, 2021;Ariyanto et al, 2021;Fahmi & Cahyono, 2021;Ghufron, 2019;Karlina Ambarwati, 2021;Miranty et al, 2022)) and have exerted an increasing influence on the L2 writing field. Barrot (2020), for instance, examined the integration of Grammarly into ESL/EFL writing.…”

Section: Peer Evaluation Versus Grammarlymentioning

confidence: 99%

“…It promotes students' use of their cognitive and metacognitive operations through noticing and provides effective grammar support in a variety of learning contexts, whether they involve international or domestic students or are adopted online or face-to-face. Guo et al (2021) examined 36 students at a prestigious Chinese university to determine the efficacy of Grammarly for EFL writers. They found that the students' grammatical errors were significantly decreased after they used feedback from Grammarly to evaluate their essay.…”

Section: Peer Evaluation Versus Grammarlymentioning

confidence: 99%

“…In addition, grammatical input is at the top of the list for essay revisions. Grammarly's comments will greatly reduce the number of grammatical errors (Guo et al, 2021). As shown in Table 1, the two forms of feedback students use to rewrite their essays are Correctness (CR) and Clarity (CL).…”

Section: Theme Three: Grammarly For Writing Processmentioning

confidence: 99%

“…It is compatible with Mac, Windows, Android, and iOS devices and is accessible through popular web browsers such as Chrome, Safari, and Firefox (Barrot, 2020). An increasing number of empirical studies have demonstrated its straightforward usefulness in enabling students and academies to write with remarkable accuracy and evaluation speed (Gao & Ma, 2020;Guo et al, 2021;Hassanzadeh & Fotoohnejad, 2021;Li, 2021;Link et al, 2020;Waer, 2021). Grammarly is beneficial for writing instruction, particularly during the revision and editing phases, as noted by Barrot (2020).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Peer and Automated Writing Evaluation (Awe): Indonesian Efl College Students’ Preference for Essay Evaluation

Ginting

Fithriani²

2022

LLT Journal

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A plethora of studies have investigated the effects of peer evaluation (PE) and automated writing evaluation (AWE) on the writing products and perspectives of English as a foreign language (EFL) students. However, few studies compare students' preferences regarding its utilization to evaluate their essays. This study aimed to determine which method EFL college students prefer for evaluating the quality of their English academic writing. For data collection, a questionnaire was distributed to determine the frequency with which students utilize feedback. Twenty students enrolled in English Education completed the questionnaire. The open-ended interview was then administered to three students in order to gain a deeper understanding of their perspectives on these two modes of evaluation. Frequency count and thematic analysis were utilized to analyze the data. The results indicate that students favor using peer feedback over Grammarly, the AWE software used in this study, for two reasons. First, they view their peers as their real audience, and second, they value their peers' comments more than Grammarly's. However, Grammarly also received positive point as the students enjoys writing with this tool. Therefore, it is believed that the combination of these two s will result in the most essay writing progress.

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Using Generative Artificial Intelligence for Language Education Research: Insights from Using OpenAI's ChatGPT

Pack

Maloney

2023

TESOL Quarterly

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Progress made in Natural Language Processing (NLP) and Artificial Intelligence (AI) in recent years has resulted in these tools becoming more accessible for individuals who lack professional training. Of particular note are large language models, such as OpenAI's GPT‐3.5. Discussions of utilizing AI for language education usually focus on the impact the technology will have on students and teachers. Less frequently the center of attention is how generative AI tools can empower researchers. The purpose of this paper is to raise awareness by demonstrating and discussing examples of how OpenAI's chatbot, ChatGPT, can be leveraged as a tool for language education researchers. After briefly introducing the use of AI generative tools in the field, this paper demonstrates how a researcher, without any understanding of NLP or AI, may use ChatGPT to assist with research through multiple means, including approaches to its use for compiling and summarizing information, and as a research assistant throughout multiple steps of research. This is followed by a discussion of potential ethical concerns of using AI for research in the field. We conclude by issuing a call for further work examining how researchers can harness the potential of this technology in ethical ways.

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How effectively can EFL students use automated written corrective feedback (AWCF) in research writing?

Cited by 55 publications

References 42 publications

Understanding the Effects of Automated Writing Corrective Feedback on L2 Writing Accuracy Across Proficiency Levels

Understanding the Effects of Automated Writing Corrective Feedback on L2 Writing Accuracy Across Proficiency Levels

Peer and Automated Writing Evaluation (Awe): Indonesian Efl College Students’ Preference for Essay Evaluation

Using Generative Artificial Intelligence for Language Education Research: Insights from Using OpenAI's ChatGPT

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