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The paper reviews prior work on this approach, reports results for eight parallel test forms produced by sixteen algorithms spanning multiple content areas and cognitive categories. Most algorithms produce items with highly homogeneous statistics, while non-homogeneous cases are analyzed to identify potential sources of differences. Applications include automatically generating new test forms with specified content and statistical requirements without relying on a pre-existing item 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differences.","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},529709,1792178268,{"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":14,"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},3985747858866,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","The Comparability of the  \nStatistical Characteristics of Testltems Generated by ComputerAlgorithms  \nRichard MeisnerRichard LuechtMark Reckase  \nDecember 1993  \nFor additional copies write:ACT Research Report SeriesP.O.Box 168lowa City,lowa 52243  \nThe Comparability of the Statistical Characteristicsof Test Items Generated by Computer Algorithms  \nRichard Meisner,Richard Luecht,and Mark ReckaseAmerican College Testing  \nPlease send requests for reprints to:  \nRichard MeisnerAmerican College Testing2201 North Dodge StreetIowa City,IA 52243  \nRunning head:The Comparability of the Statistical Characteristics  \n# The Comparability of the Statistical Characteristics\n\n## Abstract\n\nThis paper presents a study on the generation of mathematics test items using algorithmicmethods.The history of this approach is briefly reviewed,followed by a survey of researchdone to date on the statistical parallelism of algorithmically generated mathematics items.Results are presented for 8 parallel test forms generated using 16 algorithms covering a varietyof mathematics content and cognitive categories.The majority of the algorithms yielded itemsthat were very homogeneous in their statistical characteristics.Those algorithms that did notyield homogeneous items were analyzed to determine if causes for differences could bedetermined.Possible innovative applications of the algorithms include computer generation ofnew test forns with specific content and statistical specifications,without the need for a pre-existing item bank.  \nThe Comparability of the Statistical Characteristics  \n3  \n# The Comparability of the Statistical Characteristicsof Test Items Generated by Computer Algorithms\n\nMotivation for algorithmic item generation  \nEarly suggestions for the algorithmic generation of achievement test items were inresponse to at least two interrelated weaknesses perceived in educational measurement:one atthe test level,the other at the item level.An early discussion of both weaknesses was providedby Ebel(1962).  \nAt the test level,there has been persistent dissatisfaction with the lack of meaningfulinformation resulting from a purely norm-referenced interpretation of test results.At the itemlevel,it has been argued that a more informative,criterion-referenced approach to testing wouldrequire a more objective,precise approach to item writing.Ebel's early comments on thissubject are especially relevant:  \n\"The processes of test construction often appear to have more in common with artisticcreation than with scientific measurement!In this respect,educational tests are distinctlydifferent from most physical,chemical,or biological tests and measurements.In thosemore scientific fields,carefully specified measurement operations are designed to yieldhighly consistent results,almost regardless of the operator.The quantitative sophisticationof many specialists in educational measurement is displayed,not in the precision andelegance of their procedures for obtaining initial measurements,but rather in thestatistical transformations,elaborations,and analyses they are prepared to perforn onalmost any raw data given them.The term \"raw\"may be particularly appropriate whenapplied to the original data yielded by many educational tests.What we often overlookis the limited power of statistical transformations to refine these raw data and make themmore precisely meaningful.If more systematic and standardized processes of testproduction could be developed and used,our educational measurements should becomenot only more consistently reproducible,but what is perhaps even more important,theyshould become more meaningful.\"(Ebel,1962,p.22).  \nThis theme has since been elaborated by many others,including Bormuth(1970):  \n\"To those with some scientific sophistication it will be clear that what is to be advocatedis the very basic and almost self-evident idea that achievement test items,like any othermeasuring device,ought to be operationally defined.\"(p.3)  \nMany measurement specialis","cbCaihzzy4wVZtlh","https://ap.wps.com/l/cbCaihzzy4wVZtlh","pdf",661975,36,"English","# The Comparability of the Statistical Characteristics of Test Items Generated by Computer Algorithms\n## Abstract\n## Motivation for algorithmic item generation","[{\"question\":\"What is the main focus of this paper?\",\"answer\":\"The paper studies how algorithmic methods generate mathematics test items and assesses the comparability of their statistical characteristics across parallel test forms.\"},{\"question\":\"How many test forms and algorithms are analyzed?\",\"answer\":\"Results are presented for 8 parallel test forms generated using 16 algorithms.\"},{\"question\":\"What outcomes are reported about the homogeneity of item statistics?\",\"answer\":\"Most algorithms yield items that are very homogeneous in their statistical characteristics, while algorithms producing non-homogeneous items are analyzed to determine likely causes of differences.\"}]","The Comparability of the Statistical Characteristics of Test Items Generated by Computer Algorithms | PDF",1791521789,91]