[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-294574-105":59,"doc-detail-294574-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","a-study-of-a-network-flow-algorithm-and-a-noncorrecting-algorithm-for-test-assembly","A Study of a Network-Flow Algorithm and a Noncorrecting Algorithm for Test Assembly","","Evaluated two automated test assembly methods: Armstrong, Jones & Wu’s network-flow algorithm (NFA) and Luecht & Hirsch’s average growth approximation algorithm (AGAA). Algorithms were tested on ACT and ASVAB item banks with and without error in item parameters. Both matched a target test information function on the ACT bank, but only NFA matched on the ASVAB bank. AGAA, a noncorrecting method, made poor early item selections; NFA used simplex search to correct nonoptimal choices. Results show reasonable parameter error is not harmful for test assembly using these algorithms on certain banks.",{"@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/a-study-of-a-network-flow-algorithm-and-a-noncorrecting-algorithm-for-test-assembly/294574/",{"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/a-study-of-a-network-flow-algorithm-and-a-noncorrecting-algorithm-for-test-assembly/294574.png","ImageObject",300,407,{"name":92,"@type":93},"Eliana","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-18","2026-09-18",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What two algorithms are compared in the study?","Question",{"text":112,"@type":113},"The study compares Armstrong, Jones & Wu’s network-flow algorithm (NFA) and Luecht & Hirsch’s average growth approximation algorithm (AGAA) for automated test assembly.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does each algorithm perform with and without item-parameter error?",{"text":117,"@type":113},"Both algorithms match the target test information function on the ACT item bank, with and without introduced error. Only NFA matches the target on the ASVAB item bank, while AGAA does not, even without error.",{"name":119,"@type":110,"acceptedAnswer":120},"Why can noncorrecting algorithms produce poor solutions?",{"text":121,"@type":113},"Noncorrecting algorithms can become trapped in suboptimal paths in the solution space, leading to low-quality or worthless solutions. In contrast, the NFA corrects nonoptimal choices using a simplex search.","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},294574,1789693402,{"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":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":52,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":143,"preview_page_count":4},4398048949847,"https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267","# A Study of a Network-Flow Algorithm and aNoncorrecting Algorithm for Test Assembly\n\nR.D.Armstrong,D.H.Jones,and Xuan LiRutgers,The State University of New JerseyIng-Long Wu,National Yulin Institute of Technology,Taiwan  \nThe network-flow algorithm(NFA)of Armstrong,Jones,&Wu(1992)and the average growth approxi-mation algorithm(AGAA)of Luecht&Hirsch(1992)were evaluated as methods for automated test assem-bly.The algorithms were used on ACT and ASVAB itembanks,with and without error in the item parameters.Both algorithms matched a target test informationfunction on the ACT item bank,both before and aftererror was introduced.The NFA matched the target onthe ASVAB item bank;however,the AGAA did not,evenwithout error in this item bank.The AGAA is a  \nAPPLIED PSYCHOLOGICALMEASUREMENTVol.20,No.1,March 1996,pp.89-98  \n◎ Copyright 1996 Applied Psychological Measurement Inc.0146-6216/96/010089-10$1.75  \nnoncorrecting algorithm,and it made poor item selec-tions early in the search process when using the ASVABitem bank.The NFA corrects for nonoptimal choiceswith a simplex search.The results indicate that reason-able error in item parameters is not harmful for test as-sembly using the NFA or AGAA on certain types of itembanks.Index terms:algorithmic test construction,automated test assembly,greedy algorithm,heuristicalgorithm,item response theorymarginal maximumlikelihood,mathematical programming,simulation,test construction.  \nBirnbaum(1968)proposed maximizing Fisher's information to create a test based on item response theory(IRT).Lord(1980,p.23)presented iterative steps for a heuristic method to match a set of predeterminedinformation values at specific trait levels.Recently,research on automated test construction has focused onthree categories of algorithmic approaches.The first category involves the development of mathematicalprogramming models with formal constraints and solution techniques based on a simplex search(Armstrong,Jones,&Wu,1992;Baker,Cohen,&Barmish,1988;Boekkooi-Timminga,1987,1990;van der Linden &Boekkooi-Timminga,1989;Theunissen,1985,1986).The second category consists of noncorrecting ap-proaches that may not formally search for a constrained-optimal solution(e.g.,Ackerman,1989;Luecht &Hirsch,1992).A third category is a greedy heuristic followed by an interchange heuristic,or self-correctingsteepest descent approach(e.g.,Swanson &Stocking,1993).  \nNoncorrecting algorithms,although fast,can become “trapped”into searching suboptimal paths in thesolution space and may arrive at a worthless solution(Nemhauser &Wolsey,1988).Network-flow algo-rithms find a global optimum in a reasonable time;however,optimal solutions may be highly sensitive tosmall changes in the parameters defining the solution space.The objective of this study was to evaluate thequality of tests generated by a noncorrecting algorithm versus a network-flow algorithm,and to study thesensitivity of the solutions to item bank error.This study used the network-flow algorithm(NFA)of Armstronget al.(1992)and the noncorrecting average growth approximation algorithm(AGAA)of Luecht &Hirsch(1992)because these two competing algorithms are fast enough to make automated test assembly practical.  \n## Item Response Theory\n\nA test consists of m items and is selected from an item bank containing n items.Denote the response to  \nitem i as u,.For dichotomous items,u₁=1 denotes a correct response and u₁=0 denotes an incorrectresponse.Various models for the probability of correct response are available (Lord,1980).The three-parameter logistic model was used here.For trait levelθ(a real number)  \n(1)  \nwhere  \na,is the discrimination parameter for item i,  \nb,is the difficulty parameter for item i,  \nc,is the pseudoguessing parameter for item i,  \nZ;=a(θ-b),and  \nD is a scaling factor equal to 1.7.  \n## Test Information\n\nThe primary criterion (Lord,1980)for constructing tests in IRT is the test information function(TIF).The item information function is Fisher's information and i","cbCaiu0hC7o8ihAd","https://ap.wps.com/l/cbCaiu0hC7o8ihAd","pdf",629608,"English","# Item Response Theory\n# Test Information\n# A Measure of Weak Parallelism","[{\"question\":\"What two algorithms are compared in the study?\",\"answer\":\"The study compares Armstrong, Jones \\u0026 Wu’s network-flow algorithm (NFA) and Luecht \\u0026 Hirsch’s average growth approximation algorithm (AGAA) for automated test assembly.\"},{\"question\":\"How does each algorithm perform with and without item-parameter error?\",\"answer\":\"Both algorithms match the target test information function on the ACT item bank, with and without introduced error. Only NFA matches the target on the ASVAB item bank, while AGAA does not, even without error.\"},{\"question\":\"Why can noncorrecting algorithms produce poor solutions?\",\"answer\":\"Noncorrecting algorithms can become trapped in suboptimal paths in the solution space, leading to low-quality or worthless solutions. In contrast, the NFA corrects nonoptimal choices using a simplex search.\"}]","A Study of a Network-Flow Algorithm and a Noncorrecting Algorithm for Test Assembly | PDF",25]