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Yan Sun; Jamie Dyer; Jonathan Harris – Journal of Educational Computing Research, 2024
This study was grounded in the spatial computational thinking model developed by the "3D Weather" project funded by the NSF STEM+C program. The model reflects a discipline-based perspective towards computational thinking and captures the spatial nature of computational thinking in meteorology and the reliance of computational thinking on…
Descriptors: Teaching Methods, Science Instruction, Meteorology, Weather
Arik, Merve; Topçu, Mustafa Sami – Journal of Science Education and Technology, 2022
Studies maintain that computational thinking (CT) is associated with science content and scientific processes as well as with many disciplines. It is thought that designing teaching processes in which science and CT processes take place together makes science learning more meaningful. With this in mind, in this study, the researchers integrated…
Descriptors: Computation, Thinking Skills, Science Process Skills, Science Education
Chongo, Samri; Osman, Kamisah; Nayan, Nazrul Anuar – EURASIA Journal of Mathematics, Science and Technology Education, 2021
Computational thinking (CT) is one of the systematic tools in problem solving and widely accepted as an important skill in the 21st century. This study aimed to identify the effectiveness of the Chemistry Computational Thinking (CT-CHEM) Module on achievement in chemistry. This study also employed a quasi-experimental design with the participation…
Descriptors: Chemistry, Science Instruction, Thinking Skills, Achievement Tests
Braithwaite, David W.; Goldstone, Robert L. – Journal of Educational Psychology, 2013
The terms "concreteness fading" and "progressive formalization" have been used to describe instructional approaches to science and mathematics that use grounded representations to introduce concepts and later transition to more formal representations of the same concepts. There are both theoretical and empirical reasons to…
Descriptors: Mathematics Instruction, Science Instruction, Instructional Effectiveness, Teaching Methods
Scott, Fraser J. – Chemistry Education Research and Practice, 2014
This paper describes the utility of using simulated, rather than real, student solutions to problems within a peer-assessment setting and whether this approach can be used as a means of improving performance in chemical calculations. The study involved a small cohort of students, of two levels, who carried out a simulated peer-assessment as a…
Descriptors: Science Instruction, Chemistry, Simulation, Peer Evaluation
Cloonan, Carrie A.; Hutchinson, John S. – Chemistry Education Research and Practice, 2011
A Chemistry Concept Reasoning Test was created and validated providing an easy-to-use tool for measuring conceptual understanding and critical scientific thinking of general chemistry models and theories. The test is designed to measure concept understanding comparable to that found in free-response questions requiring explanations over…
Descriptors: Test Validity, Chemistry, Correlation, Multiple Choice Tests
Corlu, M. Sencer; Capraro, Robert M.; Corlu, M. Ali – Turkish Online Journal of Educational Technology - TOJET, 2011
Students need to achieve automaticity in learning mathematics without sacrificing conceptual understanding of the algorithms that are essential in being successful in algebra and problem solving, as well as in science. This research investigated the relationship between science-contextualized problems and computational fluency by testing an…
Descriptors: Foreign Countries, Statistical Significance, Scores, High School Students
Lynch, Collin F., Ed.; Merceron, Agathe, Ed.; Desmarais, Michel, Ed.; Nkambou, Roger, Ed. – International Educational Data Mining Society, 2019
The 12th iteration of the International Conference on Educational Data Mining (EDM 2019) is organized under the auspices of the International Educational Data Mining Society in Montreal, Canada. The theme of this year's conference is EDM in Open-Ended Domains. As EDM has matured it has increasingly been applied to open-ended and ill-defined tasks…
Descriptors: Data Collection, Data Analysis, Information Retrieval, Content Analysis

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