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The study investigates automated essay-scoring methods tailored to non-native learners, considering differences in proficiency, cultural context, and linguistic complexity. Using variants of a long short-term memory network, it learns essay features and compares results with Kappa, demonstrating performance comparable to state-of-the-art deep learning approaches.",{"@graph":69,"@context":113},[70,84,104],{"@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/exploring-effective-methods-for-automated-essay-scoring-of-non-native-speakers-research-article/600325/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":98,"encodingFormat":97,"isAccessibleForFree":99,"interactionStatistic":100},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/exploring-effective-methods-for-automated-essay-scoring-of-non-native-speakers-research-article/600325.png","ImageObject",300,407,{"name":92,"@type":93},"RuangKosong","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-15",true,{"@type":101,"interactionType":102,"userInteractionCount":8},"InteractionCounter",{"@type":103},"ViewAction",{"@type":105,"mainEntity":106},"FAQPage",[107],{"name":108,"@type":109,"acceptedAnswer":110},"What are the two main approaches to content scoring discussed?","Question",{"text":111,"@type":112},"Content scoring uses similarity-based methods that compare student answers to model answers, and instance-based methods that learn features linked to score levels without reference answers.","Answer","https://schema.org",{"og:url":83,"og:type":115,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":117,"canonical":83},"index,follow",{"doc_id":119,"site_id":62},600325,1792091643,{"code":4,"msg":5,"data":122},{"doc_id":119,"user_id":123,"nickname":92,"user_avatar":124,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":125,"file_id":126,"file_url":127,"file_type":128,"file_size":129,"view_count":8,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":130,"language":131,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":132,"faqs":133,"seo_title":134,"seo_description":67,"update_tm":135,"read_time":136},962090883219,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","OPEN ACCESS  \nResearch Article  \nContemporary Educational Technology, 2023, 15(4), ep475 ISSN: 1309-517X (Online)  \nExploring effective methods for automated essay scoring of non-native speakers  \nKornwipa Poonpon 1  \n 0000-0002-8349-2834  \nPaiboon Manorom 1  \n 0009-0002-2165-1120  \nWirapong Chansanam 1*  \n 0000-0001-5546-8485  \n1 Faculty of Humanities and Social Sciences, Khon Kaen University, Khon Kaen, THAILAND  \n* Corresponding author: w[irach@kku.ac.th](irach@kku.ac.th)  \nCitation: Poonpon, K., Manorom, P., & Chansanam, W. (2023) . Exploring effective methods for automated essay scoring of non-native speakers. Contemporary Educational Technology, 15(4), ep475. [https://doi.org/10.30935/cedtech/13740](https://doi.org/10.30935/cedtech/13740)  \nARTICLE INFO ABSTRACT  \n\n| Received: 19 May 2023\u003Cbr>Accepted: 15 Sep 2023\u003Cbr>INTRODUCTION | Automated essay scoring (AES) has become a valuable tool in educational settings, providing efficient and objective evaluations of student essays. However, the majority of AES systems have primarily focused on native English speakers, leaving a critical gap in the evaluation of non-native speakers’ writing skills. This research addresses this gap by exploring the effectiveness of automated essay-scoring methods specifically designed for non-native speakers. The study acknowledges the unique challenges posed by variations in language proficiency, cultural differences, and linguistic complexities when assessing non-native speakers’ writing abilities. This work focuses on the automated student assessment prize and Khon Kaen University academic English language test dataset and presents an approach that leverages variants ofthe long short-term memory network model to learn features and compare results with the Kappa coefficient. The findings demonstrate that the proposed framework and approach, which involve joint learning of different essay representations, yield significant benefits and achieve results comparable to state-of-the-art deep learning models. These results suggest that the novel text representation proposed in this paper holds promise as a new and effective choice for assessing the writing tasks of non-native speakers. The result of this study can apply to advance educational assessment practices and promote equitable opportunities for language learners worldwide by enhancing the evaluation process for non-native speakers\u003Cbr>Keywords: automated essay scoring, non-native speakers, machine learning, long short-term memory network, Thailand |\n| --- | --- |\n\nIn today’s interconnected world, the ability to communicate effectively in English has become increasingly important. English proficiency assessments play a crucial role in determining language competency, particularly for non-native speakers. Traditionally, the evaluation of essays written by these individuals has been a labor-intensive and time-consuming process, requiring human graders with expertise in language assessment. However, advancements in technology have paved the way for automated essay scoring (AES) systems, offering a promising alternative that combines the power of artificial intelligence (AI) and natural language processing (NLP) .  \nThe rapid advancement of technology and the development of NLP techniques have given rise to various automated applications across various fields. Among these applications, AES systems have emerged as an essential tool in recent decades. AES is a computer-based assessment system employing appropriate features to score or grade student responses automatically. The origins of AES research can be traced back to 1966  \nwith the introduction of the project essay grader (PEG) by Ajay et al. (1973) . To assign grades to essays, PEG evaluates various writing characteristics, including grammar, diction, and construction. In 1999, Foltz et al. introduced the intelligent essay assessor, which employed latent semantic analysis to evaluate the content and generate an overall score. Shermis et al. (20","cbCaiuK19RY8wdFU","https://ap.wps.com/l/cbCaiuK19RY8wdFU","pdf",526744,17,"English","# Introduction\n## Automated essay scoring and its limitations\n## Approaches to content scoring\n## Benefits and research directions","[{\"question\":\"What are the two main approaches to content scoring discussed?\",\"answer\":\"Content scoring uses similarity-based methods that compare student answers to model answers, and instance-based methods that learn features linked to score levels without reference answers.\"}]","Exploring effective methods for automated essay scoring of non-native speakers - Research Article | PDF",1792090464,43]