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CyWrite is an in-development automated writing evaluation program grounded in Second Language Acquisition theories, designed to support ESL learners in higher education with effective formative feedback. The study examines CyWrite’s ability to detect grammatical errors and reports on tool development approaches and grammatical analyzer performance. Evaluation on ESL undergraduate essays covers quantifiers, subject-verb agreement, articles, and run-on sentences, and compares results to Criterion, revealing stronger overall metrics and error-type-specific improvement directions.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/automated-error-detection-for-developing-grammar-proficiency-of-esl-learners/595669/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/automated-error-detection-for-developing-grammar-proficiency-of-esl-learners/595669.png","ImageObject",300,407,{"name":92,"@type":93},"Well Done","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-10-15",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is CyWrite intended to do for ESL learners?","Question",{"text":112,"@type":113},"CyWrite provides formative feedback to support autonomous learning and improvement of ESL learners’ writing skills, with a focus on detecting grammatical errors.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which grammatical error types were evaluated in the study?",{"text":117,"@type":113},"The evaluation covered four types of grammatical errors: quantifiers, subject-verb agreement, articles, and run-on sentences.",{"name":119,"@type":110,"acceptedAnswer":120},"How did CyWrite’s performance compare with Criterion?",{"text":121,"@type":113},"CyWrite demonstrated better performance metrics than the commercially available AWE tool Criterion, and further analysis of false positives and false negatives suggests how the tool can be improved.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},595669,1792167548,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},5909892210082,"https://ap-avatar.wpscdn.com/avatar/56001069da27c9f41e8?x-image-process=image/resize,m_fixed,w_180,h_180&k=1789614185077863061","calico journal (online) issn 2056–9017  \nArticle  \nAutomated Error Detection for Developing Grammar Proficiency of ESL Learners  \nHui-Hsien Feng, Aysel Saricaoglu, and Evgeny Chukharev-Hudilainen  \nAbstract  \nThanks to natural language processing technologies, computer programs are actively being used not only for holistic scoring, but also for formative evaluation of writing. CyWrite is one such program that is under development. The program is built upon Second Language Acquisition theories and aims to assist ESL learners in higher education by providing them with effective formative feedback to facilitate autonomous learning and improvement of their writing skills. In this study, we focus on CyWrite’s capacity to detect grammatical errors in student writing. We specifically report on (1) computational and pedagogical approaches to the development of the tool in terms of students’ grammatical accuracy, and (2) the performance of our grammatical analyzer. We evaluated the performance of CyWrite on a corpus of essays written by ESL undergraduate students with regards to four types of grammatical errors: quantifiers, subject-verb agreement, articles, and run-on sentences. We compared CyWrite’s performance at detecting these errors to the performance of a well-known commercially available AWE tool, Criterion. Our findings demonstrated better performance metrics of our tool as compared to Criterion, and a deeper analysis of false positivesand false negatives shed light on how CyWrite’s performance can be improved.  \nKeywords: automated writing evaluation; design-based research; error correction; evaluation of AWE systems; formative feedback; grammatical errors  \nAffiliation  \nIowa State University.  \nemail: [hhfeng@iastate.edu](hhfeng@iastate.edu) (corresponding author)  \ncalico journal vol 33.1 2016 49–70 ©2016, equinox publishing  \ndoi : 10.1558/cj.v33i1.26507  \n50 Automated error Detection  \n1. Introduction  \nThe role of explicit teaching of grammar has changed in the field of second language acquisition (SLA) throughout its history, alongside changes in theoretical views of language. While language teaching meant the teaching of grammar in the 1850s, grammar was eliminated from language classes until the late 1900s (Nassaji & Fotos, 2011; Richards & Rodgers, 2001) . With functional perspectives of grammar gaining momentum, teaching grammar explicitly bacame important again in the 1970s and 1980s (Nassaji & Fotos, 2011) . Since then, the focus on grammar has drawn the attention of teachers and researchers to learners’ grammatical errors and ways of treating them.  \nEmpirical studies suggest that language learners’ grammatical errors should be addressed through corrective feedback to help improve their grammatical accuracy (Ferris & Roberts, 2001) . Furthermore, Bitchener and Ferris (2012) and Heift (2004) argue that feedback about grammar should incorporate detailed metalinguistic explanations about the rules that the learner violates when making grammatical errors. From the practical viewpoint, providing grammatical feedback to learners is problematic for two reasons. First, locating and marking errors is a very time-consuming task for instructors. This time burden increases if the instructor needs to identify and explain specific grammatical rules to the learner while providing corrective feedback. Second, providing accurate grammatical feedback requires working knowledge of descriptive grammar and comparative linguistic typology, which some teachers might lack if they have not received sufficient training in linguistics (Ferris, 2010) .  \nOver the last few years, a growing body of research in computer-assisted language learning (CALL) has focused on responding to the challenge of providing accurate grammatical feedback to learners through the development and use of automated writing evaluation (AWE) tools based on natural language processing (NLP) and machine-learning technologies. These tools, such as Criterion, the web-ba","cbCaivstqpEdm3UR","https://ap.wps.com/l/cbCaivstqpEdm3UR","pdf",775712,22,"English","# Introduction\n## Grammar teaching in second language acquisition\n## Corrective feedback and metalinguistic explanations\n## Automated writing evaluation and grammar feedback\n## Study motivation and CyWrite overview","[{\"question\":\"What is CyWrite intended to do for ESL learners?\",\"answer\":\"CyWrite provides formative feedback to support autonomous learning and improvement of ESL learners’ writing skills, with a focus on detecting grammatical errors.\"},{\"question\":\"Which grammatical error types were evaluated in the study?\",\"answer\":\"The evaluation covered four types of grammatical errors: quantifiers, subject-verb agreement, articles, and run-on sentences.\"},{\"question\":\"How did CyWrite’s performance compare with Criterion?\",\"answer\":\"CyWrite demonstrated better performance metrics than the commercially available AWE tool Criterion, and further analysis of false positives and false negatives suggests how the tool can be improved.\"}]","Automated Error Detection for Developing Grammar Proficiency of ESL Learners | PDF",1792081602,55]