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Terzi, Ragip; Sen, Sedat – SAGE Open, 2019
Large-scale assessments are generally designed for summative purposes to compare achievement among participating countries. However, these nondiagnostic assessments have also been adapted in the context of cognitive diagnostic assessment for diagnostic purposes. Following the large amount of investments in these assessments, it would be…
Descriptors: Achievement Tests, Elementary Secondary Education, Foreign Countries, International Assessment
Evran, Derya – International Journal of Modern Education Studies, 2019
Detection of students' ability levels is one of the common aims in educational studies. Cognitive Diagnosis Modeling approach has been used recently for the purpose of ability level detection by defined Q-matrices. To evaluate students' strengths and weaknesses, determine their mastery skills, and design instructions and interventions in learning…
Descriptors: Cognitive Measurement, Models, Foreign Countries, Achievement Tests
Wetzel, Eunike; Xu, Xueli; von Davier, Matthias – Educational and Psychological Measurement, 2015
In large-scale educational surveys, a latent regression model is used to compensate for the shortage of cognitive information. Conventionally, the covariates in the latent regression model are principal components extracted from background data. This operational method has several important disadvantages, such as the handling of missing data and…
Descriptors: Surveys, Regression (Statistics), Models, Research Methodology
Stamper, John, Ed.; Pardos, Zachary, Ed.; Mavrikis, Manolis, Ed.; McLaren, Bruce M., Ed. – International Educational Data Mining Society, 2014
The 7th International Conference on Education Data Mining held on July 4th-7th, 2014, at the Institute of Education, London, UK is the leading international forum for high-quality research that mines large data sets in order to answer educational research questions that shed light on the learning process. These data sets may come from the traces…
Descriptors: Information Retrieval, Data Processing, Data Analysis, Data Collection

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