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…

The interpretation of data and construction and understanding of graphs are central practices in science; therefore, an important skill needed in the undergraduate physics laboratory is the ability to analyze data obtained from experiments. Often students are not able to reach logical deductions based on data, acquired from the experiments that…