Relevant bibliographies by topics / Automated writing evaluation (AWE)

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Automated writing evaluation (AWE)'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Automated writing evaluation (AWE)"

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Alshehri, Mona. "Integrating Automated Writing Evaluation into Saudi EFL Students’ Writing Practice." Arab World English Journal 16, no. 1 (2025): 70–87. https://doi.org/10.24093/awej/vol16no1.5.

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The increasing influence of artificial intelligence on educational methodologies has led to the emergence of AWE systems, which serve as valuable tools for enhancing writing skills through personalised feedback. This study aims to investigate the integration of AWE systems into EFL writing practices among university students, focusing on their experiences, evaluations, challenges, and satisfaction. A mixed-methods research design was conducted in two stages: the first involved 154 EFL Arabic-speaking undergraduate students at a Saudi university, while the second focused on a convenience sample
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Dr., Beata Lewis Sevcikova. "AUTOMATED ESSAY SCORING AS A PEDAGOGICAL TOOL." International Journal of Teaching & Teahcers Education 2017, no. 05 (2017): 19. https://doi.org/10.5281/zenodo.849137.

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<em>This research work offers an experiential evaluation of Automated Writing Evaluation (AWE) approach used for L2 academic writing, teaching, and learning. It examines the commercial academic writing support, online grammar, and spelling checkers available online, such as Grammarly and Marking Mate. One of the significant functions the online software performs is the generating of immediate and individualized feedback. In the case of Grammarly, it also generates a discipline-specific feedback. The objective of the study was to examine the potential support of both automated proofreaders for
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Aldosemani, Tahani I., Hussein Assalahi, Areej Lhothali, and Maram Albsisi. "Automated Writing Evaluation in EFL Contexts." International Journal of Computer-Assisted Language Learning and Teaching 13, no. 1 (2023): 1–19. http://dx.doi.org/10.4018/ijcallt.329962.

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This paper explores the literature on AWE feedback, particularly its perceived impact on enhancing EFL student writing proficiency. Prior research highlighted the contribution of AWE in fostering learner autonomy and alleviating teacher workloads, with a substantial focus on student engagement with AWE feedback. This review strives to illuminate these facets and offer critical insights on AWE effectiveness, feedback quality, reliability, and usefulness. Guided by the research questions, 16 studies were selected, adopting specific inclusion criteria to assess the effectiveness of AWE in enhanci
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Parker, Jessica L., Kimberly Becker, and Catherine Carroca. "ChatGPT for Automated Writing Evaluation in Scholarly Writing Instruction." Journal of Nursing Education 62, no. 12 (2023): 721–27. http://dx.doi.org/10.3928/01484834-20231006-02.

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Background: Effective strategies for developing scholarly writing skills in postsecondary nursing students are needed. Generative artificial intelligence (GAI) tools, such as ChatGPT, for automated writing evaluation (AWE) hold promise for mitigating challenges associated with scholarly writing instruction in nursing education. This article explores the suitability of ChatGPT for AWE in writing instruction. Method: ChatGPT feedback on 42 nursing student texts from the Michigan Corpus of Upper-Level Student Papers was assessed. Assessment criteria were derived from recent AWE research. Results:
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Zulkornain, Luqmanul Hakim, Azman Che Mat, and Nurul Ajleaa Abdul Rahman. "Does Automated Writing Evaluation (AWE) Improve Students’ Writing? Focus on Technical Aspects and Readability." International Journal of Information and Education Technology 13, no. 10 (2023): 1656–62. http://dx.doi.org/10.18178/ijiet.2023.13.10.1974.

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Automated Writing Evaluation (AWE) software has gained popularity in the field of writing related research. Most studies focused on perception and acceptance of AWEs and improvements in writing. However, this study was carried out based on the technicality of the writing which are often discussed as readability. The objectives of the study are to investigate the improvement in writing and to compare the results between the control and the experimental groups. The study employed a descriptive research design with two groups (control (n=49) and experimental n=72)) undergoing two tests (pre-test
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Defianty, Maya, Siti Zulfa, and Yatni Fatwa Mulyati. "Embedding Automated Writing Evaluation in Providing Formative Feedback." NOBEL: Journal of Literature and Language Teaching 15, no. 2 (2024): 155–70. https://doi.org/10.15642/nobel.2024.15.2.155-170.

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The pivotal role of feedback in improving students’ writing competence has been undeniable, yet there has not been any agreement about how feedback can improve learning. The advancement of technology has opened opportunities to give feedback facilitated by technology, commonly referred to as Automated Writing Evaluation (AWE). AWE-based applications help produce effective feedback mostly because they are friendly. Several studies have documented that several AWE-based applications can potentially improve students’ writing skills. Nevertheless, effective feedback constitutes more than timely fe
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Miranty, Delsa, Utami Widiati, Bambang Yudi Cahyono, and Tengku Intan Suzila Tengku Sharif. "Automated writing evaluation tools for Indonesian undergraduate English as a foreign language students' writing." International Journal of Evaluation and Research in Education (IJERE) 12, no. 3 (2023): 1705–15. https://doi.org/10.11591/ijere.v12i3.24958.

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Nowadays, many computer programs are used in the teaching of writing in the context of English as a foreign language (EFL). One of the functions of the computer programs is to provide feedback to EFL students&rsquo; writing so that the quality of their writing can be improved. This study aimed to investigate whether the use of free automated writing evaluation (AWE) tools affect undergraduate EFL students&rsquo; writing skills. In this experimental study, 35 Indonesian undergraduate students of English education department were asked to use two AWE tools, Grammarly and Grammark, in the writing
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Yuan, Ximing. "The Impact of Automated Evaluation Feedback on Students’ Writing Revision." International Journal of Languages, Literature and Linguistics 9, no. 4 (2023): 526–29. http://dx.doi.org/10.18178/ijlll.2023.9.6.464.

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To make essay scoring more manageable, Automated Writing Evaluation (AWE) programs have been developed and widely used in China. It makes immediate feedback possible and helps students do self-revision instantly. This study attempts to explore the influences exerted by automated scoring feedback on students writing revision and analyze their revision process. with Juku. Pigai AWE system as an example, the research involves 45 non-English major students in Northwestern Polytechnical University and adopts their writing samples to build a small corpus. With careful study of their modification pro
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Wilson, Joshua, and Rod D. Roscoe. "Automated Writing Evaluation and Feedback: Multiple Metrics of Efficacy." Journal of Educational Computing Research 58, no. 1 (2019): 87–125. http://dx.doi.org/10.1177/0735633119830764.

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The present study extended research on the effectiveness of automated writing evaluation (AWE) systems. Sixth graders were randomly assigned by classroom to an AWE condition that used Project Essay Grade Writing ( n = 56) or a word-processing condition that used Google Docs ( n = 58). Effectiveness was evaluated using multiple metrics: writing self-efficacy, holistic writing quality, performance on a state English language arts test, and teachers' perceptions of AWE's social validity. Path analyses showed that after controlling for pretest measures, composing condition had no effect on holisti
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Gavina, Mark Anthony, and Leilani M. Ibay-Pamo. "Effect of Automated Writing Evaluation in Higher Education Academic Writing Performance." EDULANGUE 6, no. 2 (2024): 118–37. http://dx.doi.org/10.20414/edulangue.v6i2.8300.

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This study aimed to identify the effects of Automated Writing Evaluation (AWE) in the academic writing of thirty-seven (n=37) higher education students as it is believed to address common errors encountered by higher education students in academic writing, specifically in grammar (S-V-A and verb tense). The study used a one-group pretest-posttest design that determined the specific effect of the formulated AWE treatment on the participants. The statistical evidence showed that the AWE tool (Grammarly) feedback provision, when paired with the teacher’s role as facilitator, significantly improve
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Dissertations / Theses on the topic "Automated writing evaluation (AWE)"

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Palermo, Corey John. "A Framework for Deliberate Practice| Self-Regulated Strategy Development and an Automated Writing Evaluation Program." Thesis, North Carolina State University, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10610664.

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<p> Process-based approaches to writing tend to overlook the self-regulatory skills and motivational beliefs required for proficient writing (Harris, Santangelo, &amp; Graham, 2008) and do not provide the support many students need to develop into effective writers (Graham, Harris, &amp; Mason, 2005; Harris, Graham, &amp; Mason, 2006). Additionally, restricted writing opportunities preclude the sustained deliberate practice students need to develop expertise in writing (Kellogg &amp; Whiteford, 2009). This study examined an intervention that incorporated the self-regulated strategy development
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Alvero, Aaron J. "Efficacy and Implementation of Automated Essay Scoring Software in Instruction of Literacies to High Level ELLs." FIU Digital Commons, 2016. http://digitalcommons.fiu.edu/etd/2569.

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This thesis explored the integration of automated essay scoring (AES) software into the writing curriculum for high level ESOL students (levels 3, 4, and 5 on a 1-5 scale) at a high school in Miami, Fl. Issues for Haitian Creole speaking students were also explored. The Spanish and Haitian Creole speaking students were given the option to write notes, outlines, and planning sheets in their L1. After using AES in the middle of the writing process as a revision assistant tool, 24 students responded to a Likert Scale questionnaire. The students responded positively to the AES based on the results
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(9155498), Daniel C. Ernst. "The Android English Teacher: Writing Education in the Age of Automation." Thesis, 2020.

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<p>In an era of widespread automation—from grocery store self-checkout machines to self-driving cars—it is not outrageous to wonder: can teachers be automated? And more specifically, can automated computer teachers instruct students how to write? Automated computer programs have long been used in summative writing evaluation efforts, such as scoring standardized essay exams, ranking placement essays, or facilitating programmatic outcomes assessments. However, new claims about automated writing evaluation’s (AWE) formative educational potential mark a significant shift. My project questions the
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Wang, Ying-Jian, and 王穎健. "Exploring the Effect of Using Automated Writing Evaluation in Taiwanese EFL Students'' Writing." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/73263122200659920918.

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碩士<br>義守大學<br>應用英語學系碩士班<br>99<br>Nowadays, following with the expansion of globalization, it is observed that the written text of English has been a major source of information of various kinds and gained much more important role in global communication. As a matter of fact, writing is not only used to evaluate students’ English proficiency, but also to assess their understanding of other subjects. Therefore, how to enhance and evaluate EFL learners’ writing ability has gained much attention. In recent decades, automated writing evaluation [AWE] has been frequently used in the writing class to
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Cheng, Huei-Wen, and 鄭惠文. "Utilizing Automated Writing Evaluation Software in Correcting High School Students' Compositions." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/03935291396747927552.

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碩士<br>國立臺灣師範大學<br>英語學系在職進修碩士班<br>101<br>In respect of English learning, the ability of writing well has been more and more important with the advances of the Internet and the trend of globalization. Learners expect to have more practices to prepare themselves for various occasions in which they have to write in English. However, for high schools in Taiwan, with more than 40 students in a class, grading and giving feedback on students’ writing has been a burden for English teachers in Taiwan. In recent years, there have been some automated writing evaluation (AWE) systems developed to provide l
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CHANG, GWANG-CHI, and 張光霽. "A Study on Effectiveness of Applying Automated Writing Evaluation System in English Writing for Senior Students in High School." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/w5u5dm.

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碩士<br>世新大學<br>資訊傳播學研究所(含碩專班)<br>107<br>EFL（/ESL）students have a pressing need to strengthen writing skills to pass a variety of language tests. To give students immediate feedback and effectively reduce teachers’ load, some commercial AWE（Automated Writing Evaluation）systems are now available. Although these innovative aids have become popular, few researches aiming to investigate their effects on senior high students have been conducted in Taiwan. In this study, the effects of the well-known system on the local campus, Pigai.com, were examined through a Quasi-experimental design. Two 12th gra
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Chen, Chen-Yi, and 陳貞伊. "Using Automated Writing Evaluation in a Multiple-Draft English Writing Course: Perceptions and Writing Strategy Use of Students with Different English Proficiency Levels." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/8xgzgf.

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碩士<br>國立高雄應用科技大學<br>應用外語系暨英語專業溝通與教學科技碩士班<br>104<br>The purpose of the study was to investigate attitudes toward the AWE (Criterion), writing strategy use, and the perceptions of the multiple-draft approach among the students at different proficiency levels in a writing course incorporating the use of AWE. Among the 24 English majors enrolled in a sophomore writing course at a Taiwanese university, nine voluntary students of various English proficiency levels were recruited as the participants for individual interviews. The interviews were transcribed and analyzed by the researcher. The qualita
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"Examining the Role of Linguistic Flexibility in the Text Production Process." Doctoral diss., 2017. http://hdl.handle.net/2286/R.I.45020.

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abstract: A commonly held belief among educators, researchers, and students is that high-quality texts are easier to read than low-quality texts, as they contain more engaging narrative and story-like elements. Interestingly, these assumptions have typically failed to be supported by the writing literature. Research suggests that higher quality writing is typically associated with decreased levels of text narrativity and readability. Although narrative elements may sometimes be associated with high-quality writing, the majority of research suggests that higher quality writing is associated wit
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"An Analysis of the Relationship Between 4 Automated Writing Evaluation Software and the Outcomes in the Writing Program Administrator’s “WPA Outcomes for First Year Composition”." Master's thesis, 2017. http://hdl.handle.net/2286/R.I.43990.

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abstract: My study examined Automated Writing Evaluation tools (AWE) and their role within writing instruction. This examination was framed as a comparison of 4 AWE tools and the different outcomes in the Writing Program Administrators “Outcomes Statement for First Year Composition” (the OS). I also reviewed studies that identify feedback as an effective tool within composition instruction as well as literature related to the growth of AWE and the 2 different ways that these programs are being utilized: to provide scoring and to generate feedback. My research focused on the feedback generating
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Books on the topic "Automated writing evaluation (AWE)"

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Cotos, Elena. Genre-Based Automated Writing Evaluation for L2 Research Writing. Palgrave Macmillan UK, 2014. http://dx.doi.org/10.1057/9781137333377.

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Cotos, E. Genre-Based Automated Writing Evaluation for L2 Research Writing: From Design to Evaluation and Enhancement. Palgrave Macmillan Limited, 2014.

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Cotos, Elena. Genre-Based Automated Writing Evaluation for L2 Research Writing: From Design to Evaluation and Enhancement. Palgrave Macmillan, 2014.

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Cotos, E. Genre-Based Automated Writing Evaluation for L2 Research Writing: From Design to Evaluation and Enhancement. Palgrave Macmillan Limited, 2014.

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Genre-based Automated Writing Evaluation for L2 Research Writing: From Design to Evaluation and Enhancement. Palgrave Macmillan, 2014.

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Shermis, Mark D., and Jill Burstein. Handbook of Automated Essay Evaluation: Current Applications and New Directions. Taylor & Francis Group, 2013.

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Shermis, Mark D., and Jill Burstein. Handbook of Automated Essay Evaluation: Current Applications and New Directions. Taylor & Francis Group, 2013.

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Shermis, Mark D., and Jill Burstein. Handbook of Automated Essay Evaluation: Current Applications and New Directions. Taylor & Francis Group, 2013.

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Shermis, Mark D., and Jill Burstein. Handbook of Automated Essay Evaluation: Current Applications and New Directions. Taylor & Francis Group, 2013.

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Shermis, Mark D., and Jill Burstein. Handbook of Automated Essay Evaluation: Current Applications and New Directions. Taylor & Francis Group, 2013.

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Book chapters on the topic "Automated writing evaluation (AWE)"

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Li, Mimi. "Automated Writing Evaluation." In Researching and Teaching Second Language Writing in the Digital Age. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87710-1_7.

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Cotos, Elena. "Conceptualizing Genre-Based AWE for L2 Research Writing." In Genre-Based Automated Writing Evaluation for L2 Research Writing. Palgrave Macmillan UK, 2014. http://dx.doi.org/10.1057/9781137333377_4.

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Cotos, Elena. "Automated Feedback on Writing." In Digital Writing Technologies in Higher Education. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-36033-6_22.

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AbstractAutomated Writing Evaluation (AWE) tools have not only confidently entered the scene of digital writing technologies but also secured a prominent space in teaching and learning practices due to their formative automated feedback on various traits of the writing construct. The nature and types of AWE feedback vary depending on the history and origins of the tools. Having evolved through multiple generations, they can be broadly categorized as assessment-driven and genre-based. This chapter elaborates on both of these ramifications of AWE, describing their purposes, functional specificat
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Allen, Laura K., Püren Öncel, and Lauren E. Flynn. "Expanding AWE to Incorporate Reading and Writing Evaluation." In The Routledge International Handbook of Automated Essay Evaluation. Routledge, 2024. http://dx.doi.org/10.4324/9781003397618-33.

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Guo, Qian, Ruiling Feng, and Yuanfang Hua. "A glance at automated writing evaluation (AWE) in EFL/ESL writing." In Automated Written Corrective Feedback in Research Paper Revision. Routledge, 2023. http://dx.doi.org/10.4324/9781003373315-1.

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Cotos, Elena. "Prototyping Genre-Based AWE for L2 Research Writing: The Intelligent Academic Discourse Evaluator." In Genre-Based Automated Writing Evaluation for L2 Research Writing. Palgrave Macmillan UK, 2014. http://dx.doi.org/10.1057/9781137333377_5.

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Shen, Chen, Jiwei Tian, and Sheming Qu. "Examining the Effects of Automated Writing Evaluation (AWE) Feedback on EFL Learners’ Revision and Writing Quality." In Proceedings of the 2023 4th International Conference on Education, Knowledge and Information Management (ICEKIM 2023). Atlantis Press International BV, 2023. http://dx.doi.org/10.2991/978-94-6463-172-2_60.

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Cotos, Elena. "Automated Writing Evaluation." In Genre-Based Automated Writing Evaluation for L2 Research Writing. Palgrave Macmillan UK, 2014. http://dx.doi.org/10.1057/9781137333377_3.

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Boulanger, David, and Vivekanandan Suresh Kumar. "Explainable AI and AWE." In The Routledge International Handbook of Automated Essay Evaluation. Routledge, 2024. http://dx.doi.org/10.4324/9781003397618-26.

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Sirbu, Maria-Dorinela, Robert-Mihai Botarleanu, Mihai Dascalu, Scott A. Crossley, and Stefan Trausan-Matu. "ReadME – Enhancing Automated Writing Evaluation." In Artificial Intelligence: Methodology, Systems, and Applications. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99344-7_28.

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Conference papers on the topic "Automated writing evaluation (AWE)"

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Thamrin, Andi Hidayat. "Exploring the use of automated writing evaluation (AWE) in English language education: A systematic review." In XXnd International CALL Research Conference. Castledown Publishers, 2024. http://dx.doi.org/10.29140/9780648184485-42.

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Automated Writing Evaluation (AWE) systems have emerged as indispensable tools in the realm of writing instruction, offering educators and students efficient means to assess and enhance writing skills. In recent years, the proliferation of these systems has underscored the need for a thorough understanding of their functionalities, effectiveness, and implications for educational practice. Consequently, this study endeavors to address this imperative by conducting a systematic literature review spanning the years 2018 to 2023. My overarching objectives include (a) scrutinizing the landscape of
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Liu, Liping. "Application of Automated Writing Evaluation (AWE) System Based on Intelligent Technology." In 2022 IEEE 2nd International Conference on Mobile Networks and Wireless Communications (ICMNWC). IEEE, 2022. http://dx.doi.org/10.1109/icmnwc56175.2022.10031924.

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Yeung, Steven. "A comparative study of rule-based, machine learning and large language model approaches in automated writing evaluation (AWE)." In LAK '25: The 15th International Learning Analytics and Knowledge Conference. ACM, 2025. https://doi.org/10.1145/3706468.3706566.

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Tian, Yongqiang, Zhenyang Xu, Yiwen Dong, Chengnian Sun, and Shing-Chi Cheung. "Revisiting the Evaluation of Deep Learning-Based Compiler Testing." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/542.

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A high-quality program generator is essential to effective automated compiler testing. Engineering such a program generator is difficult, time-consuming, and specific to the language under testing, thus requiring tremendous efforts from human experts with language-specific domain knowledge. To avoid repeatedly writing program generators for different languages, researchers recently proposed a language-agnostic approach based on deep learning techniques to automatically learn a program generator (referred to as DLG) from existing programs. Evaluations show that DLGs outperform Language-Specific
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Patout, Pierre-André, and Maxime Cordy. "Towards context-aware automated writing evaluation systems." In the 1st ACM SIGSOFT International Workshop. ACM Press, 2019. http://dx.doi.org/10.1145/3340435.3342722.

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Burstein, Jill, Dan McCaffrey, Beata Beigman Klebanov, and Guangming Ling. "Exploring Relationships Between Writing and Broader Outcomes With Automated Writing Evaluation." In Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications. Association for Computational Linguistics, 2017. http://dx.doi.org/10.18653/v1/w17-5011.

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Beigman Klebanov, Beata, and Nitin Madnani. "Automated Evaluation of Writing – 50 Years and Counting." In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.acl-main.697.

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Allen, Laura K., Aaron D. Likens, and Danielle S. McNamara. "A multi-dimensional analysis of writing flexibility in an automated writing evaluation system." In LAK '18: International Conference on Learning Analytics and Knowledge. ACM, 2018. http://dx.doi.org/10.1145/3170358.3170404.

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Dronen, Nicholas, Peter W. Foltz, and Kyle Habermehl. "Effective Sampling for Large-scale Automated Writing Evaluation Systems." In L@S 2015: Second (2015) ACM Conference on Learning @ Scale. ACM, 2015. http://dx.doi.org/10.1145/2724660.2724661.

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Hossain, Mehadi, and Hossen A. Mustafa. "Automated Writing Evaluation Using Sentence by Sentence Scoring Model." In 2023 International Conference on Next-Generation Computing, IoT and Machine Learning (NCIM). IEEE, 2023. http://dx.doi.org/10.1109/ncim59001.2023.10212752.

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