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The work describes an automated approach for producing, administering, and scoring multiple-choice vocabulary and comprehension questions in Project LISTEN’s Reading Tutor (2001-2002). Validation uses comparison against the Woodcock Reading Mastery Test, analyzing 69,326 multiple-choice cloze items generated during routine tutor use by 364 students in grades 1-9 across seven schools. Results show strong agreement between predicted and actual performance (R = .85).",{"@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":20,"@type":76,"position":81},"https://docshare.wps.com/document/exam/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/automated-comprehension-assessment-in-a-reading-tutor-automated-method-for-cloze-based-multiple-choice-questions/462907/",{"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-comprehension-assessment-in-a-reading-tutor-automated-method-for-cloze-based-multiple-choice-questions/462907.png","ImageObject",300,407,{"name":92,"@type":93},"Patrick","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-22","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":19},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"How does the Reading Tutor generate comprehension and vocabulary questions automatically?","Question",{"text":112,"@type":113},"It generates multiple-choice cloze items from story text, choosing target words and building suitable distractors instead of using open-ended fill-in responses.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What data was used to validate the automated assessment method?",{"text":117,"@type":113},"The method was validated against the Woodcock Reading Mastery Test by analyzing 69,326 multiple-choice cloze items produced during regular tutor use by 364 students in grades 1-9 at seven schools.",{"name":119,"@type":110,"acceptedAnswer":120},"Why were multiple-choice cloze items used rather than fill-in-the-blank blanks?",{"text":121,"@type":113},"Multiple-choice avoids the need to score unpredictable student free-form responses, shifting the challenge to generating distractors that match the target word’s properties.","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},462907,1790798807,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":19,"category_name":20,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":19,"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},549758146520,"https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470","Automated Comprehension Assessment in a Reading Tutor  \nJack Mostow, Brian Tobin, Andrew Cuneo  \nProject LISTEN1, RI-NSH 4213, 5000 Forbes Ave, Carnegie Mellon University, USA  \n[mostow@cs.cmu.edu](mostow@cs.cmu.edu)  \n[http://www.cs.cmu.edu/](http://www.cs.cmu.edu/)~listen  \nAbstract  \nCan vocabulary and comprehension assessments be generated automatically for a given text? We describe the automated method used to generate, administer, and score multiple-choice vocabulary and comprehension questions in the 2001-2002 version of Project LISTEN’s Reading Tutor. To validate the method against the Woodcock Reading Mastery Test, we analyzed 69,326 multiple-choice cloze items generated in the course of regular Reading Tutor use by 364 students in grades 1-9 at seven schools.  \nCorrelation between predicted and actual scores reached R=.85 for Word and Passage Comprehension.  \nKeywords  \nAssessment of reading comprehension and vocabulary, multiple-choice cloze tests, curriculum-based assessment, validation, Woodcock Reading Mastery Test, Reading Tutor  \n1 Introduction  \nProject LISTEN’s Reading Tutor listens to children read, and helps them learn to read (Mostow & Aist, 2001) . To balance learner control with tutorial guidance, the Reading Tutor takes turns with the student to pick from its hundreds of stories, and uses students’ assisted reading rate to adjust the story level it picks (Aist & Mostow, in press), ranging from kindergarten to grade 7 (disguised as K, A, B,…, G to avoid embarrassing poor readers) . Thus every student reads a different set of stories. Can we assess their comprehension automatically without writing (let alone validating!) comprehension questions for every story by hand? That is, how can we assess comprehension of given texts automatically to trace students’ developing vocabulary and comprehension skills?  \nExisting assessments of children’s vocabulary and comprehension such as the Woodcock Reading Mastery Test (Woodcock, 1998), Spache’s Diagnostic Reading Scales (Spache, 1981), and Gray Oral Reading Tests (Wiederholt & Bryant, 1992) use comprehension questions developed by hand for specific text passages. In contrast, curriculum-based measurement (Deno, 1985) assesses students based on material they use in the course of normal instruction. One curriculum-based measurement approach to assessing comprehension is t o prompt readers to retell what they read. Although such a prompt is easy to automate, scoring oral responses is not, because automated speech recognition is too inaccurate on such unpredictable speech.  \nSome researchers have generated cloze tests mechanically from a given text by replacing one or more words with blanks to fill in. For example, free software for one such “cloze test involves taking a document (or a document sample) of about 250 words and deleting every fifth word (these seem to be the canonical numbers,) leaving a blank in its place. The reader is then asked to fill in the missing words. In technical writing we use this as a test of readability. The idea is that there should be sufficient (local) redundancy in a document to allow a reader to score in the 50-60% range. Used in this way it measures the writer not the reader”(Drott). The cloze method was applied to the Pascal programming language to measure students ’ comprehension of computer programs  \n1 This work was supported by the National Science Foundation under Grant Number REC-9979894. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the National Science Foundation or the official policies, either expressed or implied, of the sponsors or of the United States Government. We thank other members of Project LISTEN who contributed to this work, and the students and educators at the schools where Reading Tutors recorded data.  \n(Entin, 1984). However, most words in English text (apart from highly predictable function words) a","cbCaiqVz0eWNPOFI","https://ap.wps.com/l/cbCaiqVz0eWNPOFI","pdf",232719,11,"English","# Abstract\n# 1 Introduction\n## 1.1 Approach","[{\"question\":\"How does the Reading Tutor generate comprehension and vocabulary questions automatically?\",\"answer\":\"It generates multiple-choice cloze items from story text, choosing target words and building suitable distractors instead of using open-ended fill-in responses.\"},{\"question\":\"What data was used to validate the automated assessment method?\",\"answer\":\"The method was validated against the Woodcock Reading Mastery Test by analyzing 69,326 multiple-choice cloze items produced during regular tutor use by 364 students in grades 1-9 at seven schools.\"},{\"question\":\"Why were multiple-choice cloze items used rather than fill-in-the-blank blanks?\",\"answer\":\"Multiple-choice avoids the need to score unpredictable student free-form responses, shifting the challenge to generating distractors that match the target word’s properties.\"}]","Automated Comprehension Assessment in a Reading Tutor - Automated Method for Cloze-Based Multiple-Choice Questions | PDF",1790765734,28]