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Feedback may not be sufficiently valid or pedagogically appropriate to improve overall writing quality.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What does the paper propose for automated essay feedback?",{"text":118,"@type":114},"The paper describes an exploratory method for developing feedback algorithms grounded in writing pedagogy, producing pedagogically-guided threshold algorithms.",{"name":120,"@type":111,"acceptedAnswer":121},"How do the proposed threshold algorithms relate to essay quality and feedback usefulness?",{"text":122,"@type":114},"The threshold algorithms are shown to be meaningfully related to essay quality and informative for individualized formative feedback for writers.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},267181,1789415780,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":29,"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":130,"read_time":144,"preview_page_count":4},1099523882182,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","Developing Pedagogically-Guided Threshold Algorithms for Intelligent Automated Essay Feedback  \nRod D. Roscoe, Danica Kugler1, Scott A. Crossley3, Jennifer L. Weston2, and  \nDanielle S. McNamara2  \n1 Dept. of English, 2Dept. of Psychology, Learning Sciences Institute, Arizona State University, Tempe, AZ 85287 3Dept. of Applied Linguistics/ESL, Georgia State University, Atlanta, GA 30302 {rod.roscoe, danica.kugler, jennifer.weston, [danielle.mcnamara}@asu.edu](danielle.mcnamara}@asu.edu), [sacrossley@gmail.com](sacrossley@gmail.com)  \nAbstract  \nCurrent computer based tools for writing instruction show high scoring accuracy but uncertain instructional efficacy. One explanation is that these systems may not effectively communicate valid and appropriate formative feedback. In this paper, we describe an exploratory method for developing feedback algorithms that are grounded in writing pedagogy. The resulting threshold algorithms are shown tobe meaningfully related to essay quality and informative regarding individualized, formative feedback for writers.  \nIntroduction  \nA number of computer tools have been created to facilitate educators’ ability to assess student writing and provide feedback. Grimes and Warschauer (2010) describe two kinds of systems: automated essay scoring (AES) and automated writing evaluation (AWE) . AES systems strive to assign accurate and reliable scores to essays or specific writing features (e.g., mechanics) . Scores are generated using various artificial intelligence (AI) methods, including statistical modeling, natural language processing (NLP), and Latent Semantic Analysis (LSA) (Shermis & Burstein, 2003) . More recently, AES systems have been paired with educational and classroom management tools to create AWE systems that support scoring and writing instruction. In this transition from automated scoring to writing instruction, issues of feedback design take on critical importance. Feedback is the primary means through which students can evaluate and improve their writing, and feedback quality has a profound impact. In particular, research has identified individualized, formative feedback as essential to students’ writing proficiency development (McGarrell & Verbeem, 2007; Sommers, 1982) . Formative  \nCopyright © 2012, Association for the Advancement of Artificial Intelligence ([www.aaai.org](www.aaai.org)). All rights reserved.  \nfeedback provides concrete guidance and methods for student improvement (Shute, 2008), such as strategies for generating and organizing ideas, arguments and evidence. In contrast, summative feedback evaluates performance, and may consist of grades and teacher critiques on spelling, lack of detail, weak arguments, and so on. Although both forms of feedback are useful, formative feedback is crucial for student growth because it renders the means and methods of such growth explicit. Thus, a critical question arises for developers of computer-based writing instruction tools: How can we translate computational linguistic indices and measures into formative feedback that is valid and useful for developing writers?  \nWhereas researchers have primarily focused on score accuracy (Warschauer & Ware, 2006), there have been relatively few evaluations of student improvement (e.g., Kellogg, Whiteford, & Quinlan, 2010) or the role of feedback (e.g., Roscoe, Varner, Cai, Weston, Crossley, & McNamara, 2011) . Hence, in this paper, we explore and describe a method for developing pedagogically-guided algorithms that guide formative feedback in an intelligent tutor system (ITS) for writing.  \nAccuracy, Efficacy, and Validity of AES/AWE  \nSeveral commercial AWE systems are now available, such as Criterion (scored by e rater) from the Educational Testing Service, MyAccess (IntelliMetric) from Vantage Learning, WriteToLearn (Intelligent Essay Assessor) from Pearson Inc., and WPP Online (PEG) from Educational Record Bureau. Each system adopts a different scoring approach. For example, Criterion (B","cbCairBVcLI9npvj","https://ap.wps.com/l/cbCairBVcLI9npvj","pdf",262714,"English","# Abstract\n# Introduction\n# Accuracy, Efficacy, and Validity of AES/AWE","[{\"question\":\"Why is scoring accuracy in automated essay systems not enough?\",\"answer\":\"Research suggests high scoring accuracy does not reliably translate into instructional efficacy. Feedback may not be sufficiently valid or pedagogically appropriate to improve overall writing quality.\"},{\"question\":\"What does the paper propose for automated essay feedback?\",\"answer\":\"The paper describes an exploratory method for developing feedback algorithms grounded in writing pedagogy, producing pedagogically-guided threshold algorithms.\"},{\"question\":\"How do the proposed threshold algorithms relate to essay quality and feedback usefulness?\",\"answer\":\"The threshold algorithms are shown to be meaningfully related to essay quality and informative for individualized formative feedback for writers.\"}]","Developing Pedagogically-Guided Threshold Algorithms for Intelligent Automated Essay Feedback | PDF",15]