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Aksu, Gökhan; Güzeller, Cem Oktay; Eser, Mehmet Taha – International Journal of Assessment Tools in Education, 2019
In this study, it was aimed to compare different normalization methods employed in model developing process via artificial neural networks with different sample sizes. As part of comparison of normalization methods, input variables were set as: work discipline, environmental awareness, instrumental motivation, science self-efficacy, and weekly…
Descriptors: Sample Size, Artificial Intelligence, Classification, Statistical Analysis
Bulut, Okan; Yavuz, Hatice Cigdem – International Journal of Assessment Tools in Education, 2019
Educational data mining (EDM) has been a rapidly growing research field over the last decade and enabled researchers to discover patterns and trends in education with more sophisticated methods. EDM offers promising solutions to complex educational problems. Given the rapid increase in the availability of big data in education and software…
Descriptors: Data Analysis, Educational Research, Educational Researchers, Computer Software
Finch, W. Holmes; Marchant, Gregory J. – Online Submission, 2017
A recursive partitioning model approach in the form of classification and regression trees (CART) was used with 2012 PISA data for five countries (Canada, Finland, Germany, Singapore-China, and the Unites States). The objective of the study was to determine demographic and educational variables that differentiated between low SES student that were…
Descriptors: Foreign Countries, Achievement Tests, International Assessment, Secondary School Students
Yalcin, Seher – International Journal of Progressive Education, 2018
In this study, it is aimed to distinguish the reading skills of students participating in PISA 2015 application into multi-level latent classes at the student and country level. Furthermore, it is aimed to examine how the clusters emerged at country-level is predicted by variables as students have the information and communication technology (ICT)…
Descriptors: Reading Achievement, Reading Skills, Classification, Information Technology
Özkan, Metin; Balci, Suphi; Kayan, Selman; Is, Engin – International Education Studies, 2018
The objective of the study was to make a comparison among the two countries according to the level of sufficiency of educational resources and to determine the accuracy level at which variables related to educational resources can classify the schools on the basis of countries. Relational survey model was used. The sample group of the study was…
Descriptors: Educational Resources, Educational Quality, Comparative Analysis, Comparative Education
Panyajamorn, Titie; Suanmali, Suthathip; Kohda, Youji; Chongphaisal, Pornpimol; Supnithi, Thepchai – Malaysian Journal of Learning and Instruction, 2018
Purpose: This study proposed and examined the effectiveness of e-learning content design by considering two different subjects (mathematics and reading) and areas (metropolitan and rural). This study also investigated several variables, i.e., students' satisfaction, motivation, and experience, that influenced learning abilities. Moreover, we…
Descriptors: Electronic Learning, Technology Uses in Education, Reading Instruction, Mathematics Instruction
Castejón, Alba; Zancajo, Adrián – European Educational Research Journal, 2015
This article focuses on analysing the effect of educational differentiation policies of OECD educational systems on socioeconomically disadvantaged students, based on data from PISA 2009. The analysis is conducted on the basis of a definition of two subgroups of disadvantaged students: those that achieve high scores, and those obtaining scores…
Descriptors: Disadvantaged, Educational Policy, Educational Practices, Individualized Programs
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