[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-273334-105":3,"detail-sidebar-cat-0-en-105":73,"doc-detail-273334-en":123},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":66,"head_meta":68,"extra_data":70,"updated_unix":72},105,"en","monitoring-student-achievement-with-cognitive-diagnosis-model-read-online-free","Monitoring Student Achievement with Cognitive Diagnosis Model - read online free","","This study investigates how student achievement can be monitored using cognitive diagnosis models. Longitudinal responses from the 6th, 7th, and 8th grade Mathematics subtests of the High School Placement Test (HSPT) for 2009–2011 are analyzed, using 49,933 examinees’ responses. A Q-matrix with five attributes is constructed based on mathematics experts’ specifications. Results show the largest latent class remains non-mastery across attributes for all three years, with mastery probability rising in grade 7 and falling in grade 8. High classification accuracy supports the use of HSPT outcomes for achievement monitoring.",{"@graph":14,"@context":65},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/monitoring-student-achievement-with-cognitive-diagnosis-model-read-online-free/273334/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/monitoring-student-achievement-with-cognitive-diagnosis-model-read-online-free/273334.png","ImageObject",300,407,{"name":42,"@type":43},"Marry","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-30","2026-09-15",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",16,{"@type":57,"mainEntity":58},"FAQPage",[59],{"name":60,"@type":61,"acceptedAnswer":62},"Why is HSPT considered useful for monitoring in this research?","Question",{"text":63,"@type":64},"Although HSPT is not originally designed for cognitive diagnosis, the high classification accuracy indicates the results can be used to monitor student achievement.","Answer","https://schema.org",{"og:url":32,"og:type":67,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":69,"canonical":32},"index,follow",{"doc_id":71,"site_id":7},273334,1789470915,{"code":4,"msg":74,"data":75},"success",[76,80,84,88,93,98,103,107,112,115,119],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":77,"show_sort_weight":78,"slug":79},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":81,"show_sort_weight":82,"slug":83},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Exam",70,"exam",{"id":89,"doc_module":4,"doc_module_name":25,"category_name":90,"show_sort_weight":91,"slug":92},5,"Comic",60,"comic",{"id":94,"doc_module":4,"doc_module_name":25,"category_name":95,"show_sort_weight":96,"slug":97},6,"Technology",50,"technology",{"id":99,"doc_module":4,"doc_module_name":25,"category_name":100,"show_sort_weight":101,"slug":102},7,"Healthcare",40,"healthcare",{"id":104,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":105,"slug":106},8,30,"research-report",{"id":108,"doc_module":4,"doc_module_name":25,"category_name":109,"show_sort_weight":110,"slug":111},9,"Religion & Spirituality",20,"religion-spirituality",{"id":110,"doc_module":4,"doc_module_name":25,"category_name":113,"show_sort_weight":110,"slug":114},"World Cup","world-cup",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":117,"show_sort_weight":116,"slug":118},10,"Lifestyle","lifestyle",{"id":120,"doc_module":4,"doc_module_name":25,"category_name":121,"show_sort_weight":89,"slug":122},19,"General","general",{"code":4,"msg":74,"data":124},{"doc_id":71,"user_id":125,"nickname":42,"user_avatar":126,"doc_module":4,"category_id":104,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":127,"file_id":128,"file_url":129,"file_type":130,"file_size":131,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":132,"language":133,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":134,"faqs":135,"seo_title":136,"seo_description":12,"update_tm":72,"read_time":137,"preview_page_count":4},5909892079670,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Monitoring Student Achievement with Cognitive Diagnosis Model  \nLevent YAKAR* Nuri DOĞAN ** Şenol DOST***  \nNazan SEZEN YÜKSEL****  \nAbstract  \nIn this study, it is aimed to show how student achievement can be monitored by using the cognitive diagnosis models. For this purpose, responses ofthe 6th, 7th, and 8th grade Mathematics subtests of High School Placement Tests (HSPT) in 2009, 2010, and 2011, which provide longitudinal data, were used, respectively. There were 49933 examiners ’ responses in data sets. The attributes examined by these tests were determined by the Mathematics experts, and the Q matrix consisting of five attributes was developed. As a result of the analysis, it was seen that the largest latent class for all three years consisted of those non-master for any attribute. It was observed that the probability of attribute mastery increased in the 7th grade and decreased in the 8th grade. The high classification accuracy seen as a result of the analysis applied to HSPT, which is not intended for the cognitive diagnosis, shows that the results can be used for monitoring student achievement.  \nKey Words: Cognitive diagnosis, student achievement, g-dina, attribute mastery probability, longitudinal data.  \nINTRODUCTION  \nEducation includes the efforts made to gain individuals the pre-determined and necessary behaviors related to the cognitive, affective, and psychomotor areas. Gaining targeted behaviors are not operationsthat happen at once, but require a process. It can be said that this situation is also reflected in measurement and evaluation. Although, in measurement and evaluation practices,it is very common to collect data on the extent to which the product reached at the end of the process meets the expected qualifications, contemporary educational approaches accept that products are not independent ofthe processes and interactions in the process (Kutlu, Doğan & Karakaya, 2010) . Therefore, it is necessary to measure the processes and interactions in the training process as well as the products at the end of the training process.  \nIt is observed that as the importance is given to revealing the development of individuals in the process, practices and researches aimed at this purpose increase. If it is accepted that measurement practices related to the process are generally for monitoring the development, it can be said that the studies forgathering information about the process are carried out through both national and international exams (Ministry of NationalEducation [MoNE], 2017;Organisation for Economic Co-operation and Development [OECD], 2019) . For example, through international exams such as PISA, TIMSS, and PIRLS,national-level development is tried to be monitored by making use of international comparisonsin areas such as mathematics, science and technology, and reading comprehension. Although international exams give indirect information about the educational process in general, they do not provide information about the status of individual students who are the subjects of the process and cannot provide detailed information about the effectiveness of educational programs. In this regard, it is observed that in many countries, information about the process is collected through exams held at  \n* Assist. Prof. Dr., Kahramanmaraş Sütçü İmam Uni, Faculty of Education, Kahramanmaraş-Turkey, [l_yakar@hotmail.com](l_yakar@hotmail.com), ORCID ID: 0000-0001-7856-6926  \n** Prof. Dr., Hacettepe Uni, Faculty of Education, Ankara-Turkey, [nuridogan2004@gmail.com](nuridogan2004@gmail.com), ORCID ID: 0000-0001- 6274-2016  \n*** Prof. Dr., Hacettepe Uni, Faculty of Education, Ankara-Turkey, [dost@hacettepe.edu.tr](dost@hacettepe.edu.tr), ORCID ID: 0000-0002-5762-8056  \ndifferent stages of education (OECD, 2019). For example, in the past Detection Exam of Student Achievement and today Monitoring and Evaluating Academic Skills exams aim to reveal the developments in main coursesin Turkey (MoNE, 2017) . In addition to th","cbCaib4d4g84WJbX","https://ap.wps.com/l/cbCaib4d4g84WJbX","pdf",607757,18,"English","# Abstract\n# Introduction\n## Monitoring student development through exams\n## Limitations of repeated measurement models\n## Cognitive diagnosis models (CDM) and latent class analysis","[{\"question\":\"Why is HSPT considered useful for monitoring in this research?\",\"answer\":\"Although HSPT is not originally designed for cognitive diagnosis, the high classification accuracy indicates the results can be used to monitor student achievement.\"}]","Monitoring Student Achievement with Cognitive Diagnosis Model - read online free | PDF",45]