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Jae-Sang Han; Hyun-Joo Kim – Journal of Science Education and Technology, 2025
This study explores the potential to enhance the performance of convolutional neural networks (CNNs) for automated scoring of kinematic graph answers through data augmentation using Deep Convolutional Generative Adversarial Networks (DCGANs). By developing and fine-tuning a DCGAN model to generate high-quality graph images, we explored its…
Descriptors: Performance, Automation, Scoring, Models
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Lee, Victor R.; Delaney, Victoria – Journal of Science Education and Technology, 2022
As data become more available and integrated into daily life, there has been growing interest in developing data science curricula for youth in conjunction with scientific practices and classroom technologies. However, the "what" and "how" of data science in pre-collegiate education have not yet reached consensus. This paper…
Descriptors: Data, Data Analysis, Curriculum Development, Educational Practices
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Brandin Conrath; Amy Voss Farris; Scott McDonald – Journal of Science Education and Technology, 2025
The changing landscape of geoscience learning has initiated growing interest in engaging science learners with climate data. One approach to teaching climate is the application of broadly accessible digital science curricula, which often include data tools such as visualizations, data representations, and simulations embedded within digital…
Descriptors: Earth Science, Wildlife, Science Education, Climate