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Eser, Mehmet Taha; Çobanoglu Aktan, Derya – International Journal of Curriculum and Instruction, 2021
By applying educational data mining methods to big data related to large-scale exams, functional relationships are discovered in a basic sense and hidden pattern(s) can be revealed. Within the scope of the research, to show how the self-organizing map (SOM) method can be used in terms of educational data mining, how SOM differs from other…
Descriptors: Science Instruction, Scientific Literacy, Data Analysis, Artificial Intelligence
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Garthwaite, Kathryn; France, Bev; Ward, Gillian – International Journal of Science Education, 2014
Data were gathered from 95 Year 10 students in a New Zealand secondary school to explore how the indicators of scientific literacy are expressed in student responses. These students completed an activity based around the two contexts of lighting and health. A matrix, which incorporated descriptive indicators, was developed to analyse the student…
Descriptors: Foreign Countries, Secondary School Students, Scientific Literacy, Matrices
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Grabau, Larry J.; Ma, Xin – International Journal of Science Education, 2017
Using data from the 2006 Program for International Student Assessment (PISA), we explored nine aspects of science engagement (science self-efficacy, science self-concept, enjoyment of science, general interest in learning science, instrumental motivation for science, future-oriented science motivation, general value of science, personal value of…
Descriptors: Science Education, Learning Motivation, Learner Engagement, Science Achievement
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