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L2 student engagement with automated feedback on writing: Potential for learning and issues of trust
Abstract
Abstract Recent case-study research on L2 student engagement with automated writing evaluation (AWE) feedback shows it is not simply a matter of understanding and implementing suggested changes but can instead be strongly influenced by individual and contextual factors. The present multiple-case study builds on this work by investigating the extent to which engagement supports learning from, and is influenced by trust in, AWE feedback. Six Mandarin L1 university students at different levels of English writing instruction participated, each providing a text they had written for a current course, which was submitted to the tool Grammarly for feedback. Screen-capture recordings, stimulated recalls, and interviews were used to collect information about how much of the feedback participants viewed, which of it they accepted, which they ignored or modified, and why they did so. Findings showed participants adopting a proofreading as opposed to a learning orientation toward the feedback, perhaps because most of it included specific corrections, thus requiring little work on the user’s part. Whereas previous studies attributed more successful engagement with AWE feedback to higher language proficiency, the present research showed different dimensions of trust in Grammarly’s feedback to be determining factors.
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In the citing paper
“Results showed high engagement, increased agency in tool use, and flexible integration of AI-generated text, contributing to both linguistic creativity and learner autonomy.”
From AI-Integrated Scaffolding to Enhance Agency and Creativity in K-12 English Language Learners: A Systematic Review (2025) · cites this paper as [34]
In the citing paper
“Results showed high engagement, increased agency in tool use, and flexible integration of AI-generated text, contributing to both linguistic creativity and learner autonomy.”
From AI-Integrated Scaffolding to Enhance Agency and Creativity in K-12 English Language Learners: A Systematic Review (2025) · cites this paper as [34]
In the citing paper
“Results showed high engagement, increased agency in tool use, and flexible integration of AI-generated text, contributing to both linguistic creativity and learner autonomy.”
From AI-Integrated Scaffolding to Enhance Agency and Creativity in K-12 English Language Learners: A Systematic Review (2025) · cites this paper as [34]
In the citing paper
“Ranalli [34] provided important insights into the role of automated grammar support, finding that precise feedback on language use led to more sophisticated sentence structures without compromising student agency in the revision process.”
From AI-Integrated Scaffolding to Enhance Agency and Creativity in K-12 English Language Learners: A Systematic Review (2025) · cites this paper as [34]
In the citing paper
“This hands-on approach helped students develop both procedural and metacognitive skills, allowing them to make more informed decisions about integrating AI feedback into their writing.”
From AI-Integrated Scaffolding to Enhance Agency and Creativity in K-12 English Language Learners: A Systematic Review (2025) · cites this paper as [34]
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“Some researchers, such as Ranalli [30], went further and strongly criticized the use of AWE tools in the language classroom and pointed out that these tools “ did not live up to expectations ” (p. 2).”
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“In addition, the instructor ’ s willingness to apply AWF (e.g. Jiang et al ., 2020), perceived trust in AWF (e.g. Ranalli, 2021; Zhai & Ma, 2021), perceived ease of use (R. Li, 2021), and self-efficacy (e.g. Li et al ., 2019) are all found to play a role in users ’ interaction with AWF.”
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“Researchers also note challenges such as the varied level of technological readiness among teachers and students as well as concerns regarding accuracy, biases, accountability, transparency, and ethics (e.g., Kohnke et al., 2023; Memarian & Doleck, 2023; Ranalli, 2021).”
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“In previous studies, researchers identified that students used similar cognitive strategies, such as noticing and understanding (e.g. Zhang 2017; Koltovskaia 2020; Ranalli 2021).”
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“Previous studies have generally attributed the individual differences of learners to language pro fi ciency (Zhang and Hyland 2018; Ranalli 2021).”
InfluentialHow Teacher and Grammarly Feedback Complement One Another in Myanmar EFL Students’ Writing
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“This finding underlines the great potential for integrating Grammarly feedback into writing instruction, supplementing teacher feedback, as reported in previous studies by O’Neill and Russell (2019b), Ventayen and Orlanda-Ventayen (2018), and Ranalli (2021).”
ResultZou, S., Guo, K., Wang, J., & Liu, Y. (2026) · Computer Assisted Language Learning · doi:10.1080/09588221.2024.2447279 · Cited by 79
“For example, Ranalli (2021) revealed that students’ distrust of Grammarly-generated feedback led to a rushed and superficial approach when addressing the feedback.”
Using automated corrective feedback tools in language learning: a review study
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“…’ Neill & Russell, 2019b; Parra & Calero, 2019; John & Woll, 2020; Koltovskaia, 2020; Tabrizi & Etemad, 2021; Guo et al., 2021; Thi & Nikolov, 2021; Ranalli, 2021; Barrot, 2021) Spelling (Blázquez & Fan, 2019; Chacón-Beltrán, 2016; Lawley, 2016; Rimbar, 2017; Tight, 2017; Lin et al., 2017)…”
InfluentialZhang, Z., & Hyland, K. (2023) · Computer Assisted Language Learning · doi:10.1080/09588221.2023.2256815 · Cited by 86
“We adopted a multiple-case study design as an established means of gathering rich data to analyse students’ experiences of language learning (Duff, 2008) and responses to feedback (e.g. Han & Hyland, 2015; Ranalli, 2021).”
InfluentialAn investigation on the use of automated feedback in Turkish EFL students’ writing classes
Han, T., & Sarı, E. (2022) · Computer Assisted Language Learning · doi:10.1080/09588221.2022.2067179 · Cited by 64
“However, since other studies (e.g., Guo et al., 2021; Jiang & Yu, 2020; Ranalli, 2021) found that AWE systems might mislabel a correct usage as erroneous, the second author of the study and an independent researcher checked the accuracy of errors identified by Criterion following the procedure…”
ResultYeung, S. (2025) · Journal of Second Language Writing · doi:10.1016/j.jslw.2025.101203 · Cited by 70
Zhang, Z., & Hyland, K. (2023) · Assessing Writing · doi:10.1016/j.asw.2023.100784 · Cited by 89
Bao, C., Zhang, L. J., & Dixon, H. R. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.710736 · Cited by 64
“…a good understanding of internal factors of students (e.g., backgrounds, attitudes, confidence, and motivations of students) (Ferris et al., 2013; Ranalli, 2021); (3) student engagement is subject to change over time, which indicates that student engagement can be either fostered or Frontiers…”
BackgroundInfluentialAutomated writing evaluation use in second language classrooms: A research synthesis
Karatay, Y., & Karatay, L. (2024) · System · doi:10.1016/j.system.2024.103332 · Cited by 49
“Lastly, the synthesis revealed that certain variables, such as social and personal factors influencing AWE engagement (e.g., Bru-dermann et al., 2021) and the effect of writing task or text genre on AWE engagement (e.g., Ranalli, 2021), were not systematically considered in the reviewed studies.”
InfluentialZhang, Z., & Xu, L. (2022) · Journal of Multilingual and Multicultural Development · doi:10.1080/01434632.2022.2102175 · Cited by 57
“…Phakiti 2014; Dikli and Bleyle 2014), a few recent studies have viewed AWE systems as a supplement to teacher-student or student-student interaction rather than a complete replacement, focusing on student engagement with AWE feedback in their writing process (Ranalli 2021; Zhang and Hyland 2018).”
InfluentialBarrot, J. S. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-12083-y · Cited by 47
“For instance, mixed results were frequent regarding AWE’s impact on student engagement because of the individual, contextual, affective and cognitive factors that come into play (Koltovskaia, 2020; Ranalli, 2021; Shi, 2021).”
Yamashita, T. (2024) · Research Methods in Applied Linguistics · doi:10.1016/j.rmal.2024.100133 · Cited by 61
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Thi, N. K., Nikolov, M., & Simon, K. (2022) · Innovation in Language Learning and Teaching · doi:10.1080/17501229.2022.2122476 · Cited by 45
“Therefore, we examined the accuracy of Grammarly feedback following the work of Ranalli (2021) and classified it into three categories: accurate, inaccurate, and indeterminate feedback.”
BackgroundMethodExploring Autonomy in the AI Wilderness: Learner Challenges and Choices
Alm, A. (2024) · Education Sciences · doi:10.3390/educsci14121369 · Cited by 38
Michel, M. C., Menke-Bazhutkina, I., Abel, N., & Strobl, C. (2025) · Journal of Second Language Writing · doi:10.1016/j.jslw.2025.101185 · Cited by 50
Understanding human-AI trust in education
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“As educational technologies have become central to students ’ learning experiences, understanding the development of trust between students and these systems has become important for their effective use and adoption [6 – 9].”
InfluentialA Theoretical Assessment Ecosystem for a Digital-First Assessment - The Duolingo English Test
Burstein, J. C., LaFlair, G. T., Kunnan, A. J., & von Davier, A. A. (2022) · doi:10.46999/kiqf4328 · Cited by 31
“See Ranalli (2021)‘s findings that suggest that language learners’ experience with technology may influence their trust in technology.”
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Derakhshan, A. (2026) · Journal of Second Language Writing · doi:10.1016/j.jslw.2026.101304 · Cited by 29
The Platformization of Writing Instruction: Considering Educational Equity in New Learning Ecologies
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“Finally, only a handful of studies examined what we called participants’ (g) sensemaking with AI, or how various individual and contextual factors shaped the ways they engaged with the tool, feedback, or writing process (e.g., Ranalli, 2021).”
BackgroundLuo, M., Hu, X., & Zhong, C. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13488-7 · Cited by 39
“Nevertheless, some researchers argued that in real-time practice, the retention of AI-feedback-assisted writing is not satisfying (Stevenson & Phakiti, 2014), since some students only used AI feedback tool for proofreading “with no or little cognitive processing” (Ranalli, 2021).”
Has artificial intelligence rendered language teaching obsolete?
Handley, Z. L. (2024) · Modern Language Journal · doi:10.1111/modl.12929 · Cited by 35
Cheng, X., & Zhang, L. J. (2024) · The Asia-Pacific Education Researcher · doi:10.1007/s40299-024-00877-8 · Cited by 31
“…education, researchers have suggested that teacher feedback and AWE feedback should be integrated to enhance the effectiveness of feedback for L2 writing, considering the affor-dances and limitations of the two feedback sources (Link et al., 2022; Ranalli, 2021; Thi & Nikolov, 2022).”
InfluentialYu, S., Yuan, K., & Wu, P. (2022) · Journal of Second Language Writing · doi:10.1016/j.jslw.2022.100961 · Cited by 24
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“This includes validating the claims made by software companies and holding them accountable (Chapelle et al., 2015; Ranalli, 2021).”
MethodStudents’ Reliance on AI in Higher Education: Identifying Contributing Factors
Pitts, G., Rani, N., Mildort, W., & Cook, E.-M. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-12773-0_9 · Cited by 64
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A systematic review of Grammarly in L2 English writing contexts
Dizon, G., & Gayed, J. M. (2024) · Cogent Education · doi:10.1080/2331186x.2024.2397882 · Cited by 19
“…under the research topic of Learner Perceptions include Almusharraf and Alotaib (2022), Barrot (2023), Ebadi et al. (2022), Ebyary (2022), Fan (2023), Miranty and Widiati (2021), Parra and Calero (2019), Ranalli (2021), Thi and Nikolov (2022), Ventayen & Orlanda-Ventayen (2018), and Zaini (2018).”
Elementary English learners’ engagement with automated feedback
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L2 learners’ engagement with AI-generated feedback on writing
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“To assist teachers in providing frequent and timely feedback to large numbers of students, various automated writing evaluation (AWE) tools (Ranalli, 2021) have been developed to provide automated written corrective feedback (Ranalli, 2019) on students ’ writing.”
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Nurkamto, J., Prihandoko, L. A., Putro, N. H. P. S., & Purwati, O. (2024) · Studies in English Language and Education · doi:10.24815/siele.v11i1.28928 · Cited by 52
Kao, C.-W., & Reynolds, B. L. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-03522-3 · Cited by 26
“Because the role of trust in feedback sources has been claimed to in fl uence learners ’ writing performance, there has been a clarion call for experimental studies to be conducted to verify the effect of trust in feedback on students ’ writing performance (Ranalli, 2021).”
InfluentialYu-peng, L., & Yu, Z. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-03861-1 · Cited by 23
“In previous studies on automated writing evaluation and feedback quality, trust in feedback refers to the degree to which learners believe automated feedback tools provide valuable comments and suggestions (e.g., Ranalli 2021).”
Amani, N., & Bisriyah, M. (2025) · IJELTAL (Indonesian Journal of English Language Teaching and Applied Linguistics) · doi:10.21093/ijeltal.v10i1.1942 · Cited by 17
“Students have been shown to heavily rely on AI for superficial functions such as grammar checking, spelling correction, and paraphrasing, and not for more substantial and challenging learning exercises (Le, 2023; Ranalli, 2021).”
Cotos, E. (2023) · Springer eBooks · doi:10.1007/978-3-031-36033-6_22 · Cited by 15
How L2 student writers engage with automated feedback: A longitudinal perspective
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Ebyary, K. E., Shabara, R., & Boraie, D. (2024) · Language Testing in Asia · doi:10.1186/s40468-024-00312-1 · Cited by 22
“Furthermore, the role of teacher feedback has been well documented (e.g., Sermsook et al., 2017), but little is known about the role of AI-operated feedback in noticing errors and uptake (Barrot, 2023a; Ranalli, 2021).”
Namaziandost, E. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100446 · Cited by 15
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BackgroundVisibilizing invisible engagement behind students’ no-revision operation to written feedback
Fu, X., Yang, C., & Zhang, T. (2024) · Language Teaching Research · doi:10.1177/13621688241227666 · Cited by 14
“Many recent studies also notice students’ unaction to some AWE feedback, as in Lavolette et al. (2015), Zhang (2016), Bai and Hu (2017), Ranalli (2018, 2021), and Koltovskaia (2020); and to peer feedback as in Yu et al. (2018), Fan and Xu (2020), and Aben et al. (2022) as well, to name a few.”
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“Ranalli (2021) noted that students’ willingness to accept feedback from AI tools depends on their trust in the tool.”
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Ginting, R. S. B., & Fithriani, R. (2022) · LLT Journal A Journal on Language and Language Teaching · doi:10.24071/llt.v25i2.4879 · Cited by 10
“First, the correlation between automated and human grading of student writing (Bridgeman et al., 2012); and second, the effects of automated analysis on student writing performance (Jiang et al., 2020; Ranalli, 2021).”
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AI and VR Converge: The Future of Language Learning in an Emerging Metaverse
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Overstreet, M., Akhmedjanova, D. R., & Vaccino‐Salvadore, S. (2023) · Journal of Second Language Writing · doi:10.1016/j.jslw.2023.101019 · Cited by 9
EFL Learner Engagement in Automatic Written Evaluation
Fu, H., & Liu, X. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.871707 · Cited by 15
“…students with different language proficiency were employed to analyze students’ engagement with AWCF qualitatively in the use of stimulated recall, semi-structured interviews and retrospective verbal reports, reflective journals, etc. (Han, 2017; Koltovskaia, 2020; Zhang, 2020; Ranalli, 2021).”
BackgroundMethodKloppers, J. (2023) · International Journal of Computer-Assisted Language Learning and Teaching · doi:10.4018/ijcallt.325792 · Cited by 17
Establishing Trustworthy Artificial Intelligence in Automated FeedbackPreprint
Lin, J., Sha, L., Li, Y., Gašević, D., & Chen, G. (2022) · EdArXiv (OSF Preprints) · doi:10.35542/osf.io/5efxn · Cited by 10
Less-proficient EFL students' use of Grammarly in writing: Behavior, cognition, and affect
Anastasia, W., Murtisari, E. T., & Isharyanti, N. (2024) · The JALT CALL Journal · doi:10.29140/jaltcall.v20n1.1089 · Cited by 7
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“First, as comments are provided by the instructor, human-delivered SWCF may be perceived as trustable, which is said to increase revision engagement (Ranalli, 2021), by learners.”
Designing Language Learning Experiences With Generative AI Tools
Czerkawski, B. C. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-0872-1.ch015 · Cited by 7
Research impacts and trends in feedback literacy: a bibliometric analysis
Liang, X., Soh, S. B., Hamzah, N. H., & Wang, P. (2024) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2024.2407945 · Cited by 9
“Recent studies have highlighted the importance of student engagement with feedback in L2 writing (e.g. Zhang and Hyland 2018; Fan and Xu 2020; Ranalli 2021).”
Xu, Q., & Wang, L. (2025) · Innovation in Language Learning and Teaching · doi:10.1080/17501229.2025.2490100 · Cited by 6
“The findings underscore both the importance of accurate and appropriately aligned AF and the need for learners to develop metacognitive skills in AI-assisted learning environments (Ranalli 2021).”
GenAI in Academic Writing- Empowering Learners or Redefining Traditional Pedagogical Practices?
Zaheer, S., Ma, C., Zhu, Y., & Vasinda, S. (2025) · International Journal of Artificial Intelligence · doi:10.4018/ijaitl.373582 · Cited by 12
“The impact of GenAI tools on improving linguistic accuracy is well documented, with platforms such as Grammarly and Criterion providing precise, immediate feedback to address common writing challenges (Liu et al., 2023; Ranalli, 2021).”
InfluentialChen, J., & Huang, K. (2025) · Computer Assisted Language Learning · doi:10.1080/09588221.2025.2508805 · Cited by 7
Corrective Feedback of AI Tools in Writing and Translation
Jomaa, N. J. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-9511-0.ch010 · Cited by 6
Wilson, J., Cordero, T. M. C., Potter, A., Myers, M., MacArthur, C. A., Beard, G., Fudge, E. A., Raiche, A., & Ahrendt, C. (2024) · International Journal of Changes in Education · doi:10.47852/bonviewijce42024011 · Cited by 5
Yan, Z., Jalaluddin, I. B., Mamat, R. B., & Zhao, T. (2025) · Journal of Language Teaching and Research · doi:10.17507/jltr.1603.20 · Cited by 5
“Some scholars also express concerns over the potential misuse of AWE, such as relying solely on automated feedback without incorporating certain human evaluations (Ranalli, 2021).”
Student engagement with oral feedback on second language learning: A methodological review
Liu, J., & Su, W. (2025) · System · doi:10.1016/j.system.2025.103752 · Cited by 6
Yan, X., & Huang, B. H. (2025) · TESOL Quarterly · doi:10.1002/tesq.70043 · Cited by 10
Beyond error correction: lower-proficiency EFL learners’ engagement with Grammarly
Murtisari, E. T., Januardi, J. I., Bonar, G., & Kurniawan, D. (2025) · Language Awareness · doi:10.1080/09658416.2025.2585004 · Cited by 12
“Ranalli (2021) found that trust, rather than proficiency, shaped feedback engagement among Chinese EsL students (B2–C1), highlighting the complex, contextual nature of engagement (Hiver et al., 2024).”
InfluentialStudent Engagement With AI Feedback: Evidence From Ethnic Minority Students in China
Yang, F., Li, K., & Qian, L. (2025) · International Journal of Applied Linguistics · doi:10.1111/ijal.70066 · Cited by 5
Li, X., Wang, X., & Daems, J. (2026) · Computer Assisted Language Learning · doi:10.1080/09588221.2026.2631658 · Cited by 5
Using Online Tools to Help Students Elevate Lexical Range in English-as-a-Foreign-Language Writing
Nguyen, B. T. T. (2023) · RELC Journal · doi:10.1177/00336882231217528 · Cited by 5
“It was utilized largely for proofreading purposes rather than contributing to learning (Ranalli, 2021).”
Enhancing the English writing skills of in-service students using marking mate automated feedback
Huynh-Cam, T.-T., Agrawal, S., Bui, T.-T., Nalluri, V., & Chen, L. (2023) · Asia Pacific Education Review · doi:10.1007/s12564-023-09904-7 · Cited by 8
“…& Wilson, 2020) students in the United States; undergraduate students in Taiwan (Liao, 2016), Japan (Suzuki et al., 2019), the United States (Ranalli, 2021), Saudi Arabia (Aluthman, 2016), Hong Kong (Cheng, 2017), China (Zhang & Hyland, 2018), and Bahraini (Wali & Huijser, 2018);…”
Advancing Language Assessment with GPT: Is It Nonnative-Language Friendly?
Won, D.-O., Shin, Y. K., Kim, H.-J., & Yoo, I. W. (2025) · Language Assessment Quarterly · doi:10.1080/15434303.2024.2444349 · Cited by 5
“These systems, driven by natural language processing algorithms, provide diagnostic scores and comprehensive feedback on written performances (e.g., McCarthy et al., 2022; Ragheb Al-Ghezi et al., 2023; Ranalli, 2021).”
Chon, Y. V., & Shin, D. (2026) · Journal of Computer Assisted Learning · doi:10.1002/jcal.70259 · Cited by 8
Jian-hua, Z., & Zhang, L. J. (2025) · International Journal of Applied Linguistics · doi:10.1111/ijal.12822 · Cited by 5
Yao, G., & Liu, Z. (2026) · Assessing Writing · doi:10.1016/j.asw.2026.101045 · Cited by 8
Jiang, Y., Klebanov, B. B., Hao, J., Deane, P., & Livne, O. E. (2025) · Journal of Computer Assisted Learning · doi:10.1111/jcal.70014 · Cited by 5
Exploring student interpreters’ engagement with different sources of feedback on note-taking
Xue, R., & Liu, Q. (2024) · Innovations in Education and Teaching International · doi:10.1080/14703297.2024.2354739 · Cited by 5
“…on feedback sources, concentrating on teachers (e.g. Han & Hyland, 2015; Zheng & Yu, 2018), peers (e.g. Geng & Yu, 2023; Han & Yang, 2021), automated systems (e.g. Ranalli, 2021; Y. Xu & Han, 2020), and multiple-sources interaction environment (e.g. Z. Zhang & Hyland, 2022; Y. Zhang & Jiang, 2022).”
Response to written commentary in preparation for high-stakes second language writing assessment
Pearson, W. S. (2022) · Asian-Pacific Journal of Second and Foreign Language Education · doi:10.1186/s40862-022-00145-6 · Cited by 4
“These figures are higher than studies of teacher WFC in tertiary-level process writing environments (Christiansen & Bloch, 2016; Conrad & Goldstein, 1999; Ene & Upton, 2014; Hyland, 2003; Nurmukhamedov & Kim, 2009; Ranalli, 2021; Zhang & Hyland, 2018), where comments across multiple drafts serve as scaffolding to help…”
BackgroundResultLee, S. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-0872-1.ch013 · Cited by 4
Review of Feedback in Automated Essay ScoringPreprint
Jong, Y.-J., Kim, Y.-J., & Ri, O.-C. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4462105 · Cited by 4
Taguba, H., & Plata, S. M. (2025) · Language Testing in Asia · doi:10.1186/s40468-025-00362-z · Cited by 3
“Most studies on learner engagement have focused on university contexts (Zhang & Hyland, 2018; Zhang & Hyland, 2022; Man et al., 2021; Zhang, 2022; Koltovskaia & Mahapatra, 2022; Ranalli, 2021).”
Lee, M., Jang, E. E., & Hannah, L. (2025) · TESOL Quarterly · doi:10.1002/tesq.70032 · Cited by 8
Li, X., Wang, W., & Gu, L. (2025) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2025.2587925 · Cited by 4
“Students may allocate a hierarchy of trust and value to different feedback processes based on the feedback providers’ qualities, regardless of the content of the feedback message received (Ranalli 2021; Zhou et al. 2021).”
Haddadian, G., Kim, M. K., & Haddadian, N. (2025) · Research Synthesis in Applied Linguistics · doi:10.1080/29984475.2025.2598266 · Cited by 3
Soyoof, A., Reynolds, B. L., Rassaei, E., Kao, C.-W., & Ha, X. V. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100530 · Cited by 7
Metadata
- Authors
- Ranalli, J.
- Affiliations
- Iowa State University (US)
- Corresponding author
- Ranalli, Jim
- ORCID
- Ranalli, Jim
- Venue
- Journal of Second Language Writing
- Publisher
- Elsevier BV
- Published
- 2021-06
- Volume
- 52
- Pages
- 100816
- ISSN
- 1060-3743 (ISSN-L)
- OpenAlex
- W3148248532
- Semantic Scholar
- bc20929cd12bf53ca312d3549b91fc4e442bebd3
- Cited by
- 184
- References
- 51
- Citations by year
- 2026: 68 · 2025: 46 · 2024: 30 · 2023: 18 · 2022: 19 · 2021: 3
- FWCI
- 69.13
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Journal of Second Language Writing (publishedVersion)
- Topics
- Social Sciences › Arts and Humanities › Language and Linguistics › EFL/ESL Teaching and Learning
- Social Sciences › Education › Student Assessment and Feedback
- Social Sciences › Education › English Language Learning and Teaching
- Keywords
- automated writing evaluation, student engagement with feedback, Grammarly, second language writing, learning orientation, feedback acceptance, English as a foreign language
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