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Mathematics subtest questions and answers from a national exam were analyzed based on the response patterns of 20,154 students. Data mining rules were produced using RapidMiner with the FP-Growth algorithm. Results indicate students at any MATH taxonomy skill level could perform skills in subcategories at a high rate, with later learning areas strongly linked to earlier ones, except for probability of simple events.",{"@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/determination-of-association-rules-for-mathematical-skills/588274/",{"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/determination-of-association-rules-for-mathematical-skills/588274.png","ImageObject",300,407,{"name":92,"@type":93},"Aditya","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-19","2026-10-15",true,{"@type":102,"interactionType":103,"userInteractionCount":14},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the aim of the study?","Question",{"text":112,"@type":113},"To classify middle school mathematics course subjects and skills using MATH Taxonomy and determine their relationships.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What data and method were used to derive association rules?",{"text":117,"@type":113},"Mathematics subtest question-answer data from a national exam for 20,154 students was analyzed using RapidMiner and the FP-Growth algorithm.",{"name":119,"@type":110,"acceptedAnswer":120},"What were the main findings?",{"text":121,"@type":113},"Students with any level of MATH taxonomy-related skill could perform skills in subcategories at high rates, and knowledge in later learning areas corresponded strongly to earlier areas; this pattern did not hold for probability of simple events.","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},588274,1792427641,{"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":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":36,"preview_page_count":4},962085564549,"https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45","DETERMINATION OF ASSOCIATION RULES FOR  \nMATHEMATICAL SKILLS  \nHanife Merve ERDOĞAN, Nazan SEZEN YÜKSEL  \nAbstract: The aim of this study is to classify the subjects and skills of middle school mathematics course in the context of MATH Taxonomy and to determine their relations. For this purpose, the questions and answers related to the mathematics subtest of a national exam were analyzed over the answers of 20154 students. The study continued with the analysis of the data set containing the answer patterns of the students according to the rules of association of data mining methods by using RapidMiner program and FP-Growth algorithm. The results show that students with any level of skill in MATH taxonomy related to mathematical skills could perform skills in subcategories at a high rate. Students who have attainment in the learning areas provided later in the mathematics program had a high rate of knowledge in the previously provided learning areas.  \nHowever, this result is not valid for Probability of Simple Events.  \nKey words: mathematical skills, data mining, association rules, FP-Growth algorithm, RapidMiner.  \n1. Introduction  \nData is produced in almost every moment of daily life with technological developments. The data produced is often stored unconsciously and this data may contain various information that is not apparent at first glance. Different and confidential information can only be accessed after processing, analyzing, and interpreting the big data obtained. The process of accessing information from this data is defined as data mining. The data mining process reveals information that is hidden in large data sets and presents meaningful and useful patterns to users (Han, Kanber & Pei, 2012) . Processed, analyzed, and interpreted data is used in different fields, such as health (Yıldırım, Uludağ, &Görür, 2008), engineering (Eker, 2016), insurance (Söylemez, Doğan & Özcan, 2016) and education (KurtPehlivanoğlu & Duru, 2015; Şevgin 2020) . This process in education is expressed as educational data mining.  \nAs seen in Figure 1, the cycle of educational data mining is illustrated by Romero and Ventura (2007, p.136) . The data processed in educational data mining may be related to students, teachers, administrators, or parents. The outputs obtained at the end of the data mining process may be used by one of these target groups who produce this data. It is expected that the outputs will provide meaningful, qualified, useful information to people who can be expressed as stakeholders of the education system (Bilen et al., 2014) .  \nFigure 1. The application of data mining in education (Romero & Ventura, 2007, p.136)  \nSimilarly, as demostrated in Figure 2, García, Romero, Ventura and Castro (2011), who defined educational data mining as the process of converting raw data obtained from educational systems into information that educational software, developers, teachers and researchers can use, described this process in a more cyclical way:  \nFigure 2. Educational data mining process (García vd., 2011)  \nStudents produce educational data in classrooms with online or written exams such as Basic Education to Secondary Education Transition (TEOG) exam. TEOG exam, which was a central examination conducted throughout the country, could be considered an important data provider in the Turkish education system. TEOG exam, which focused on the simultaneous implementation of educational programs, differed in this sense from many measurement and evaluation applications (PISA, TIMSS, PIRLS) that are applied internationally and focus on skills while dealing with the acquisition of achievements in educational programs. TEOG also disseminated the evaluation of achievement throughout the process and ensured the simultaneous implementation of the curriculum across the country. TEOG had been implemented since 2013-2014 academic year and terminated as of 2017- 2018 academic year. Within the scope of this exam, over a million 8th grade stu","cbCaitP02p2tYZAx","https://ap.wps.com/l/cbCaitP02p2tYZAx","pdf",613831,16,"English","# Introduction","[{\"question\":\"What is the aim of the study?\",\"answer\":\"To classify middle school mathematics course subjects and skills using MATH Taxonomy and determine their relationships.\"},{\"question\":\"What data and method were used to derive association rules?\",\"answer\":\"Mathematics subtest question-answer data from a national exam for 20,154 students was analyzed using RapidMiner and the FP-Growth algorithm.\"},{\"question\":\"What were the main findings?\",\"answer\":\"Students with any level of MATH taxonomy-related skill could perform skills in subcategories at high rates, and knowledge in later learning areas corresponded strongly to earlier areas; this pattern did not hold for probability of simple events.\"}]","DETERMINATION OF ASSOCIATION RULES FOR MATHEMATICAL SKILLS | PDF",1792064894]