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…

Visual representations of data are widely used for communication and understanding, particularly in science, technology, engineering, and mathematics (STEM). However, despite their importance, many people have difficulty understanding data-based visualizations. This work presents a series of three studies that examine how understanding time-based…

Graphing is an important practice for scientists and in K-16 science curricula. Graphs can be constructed using an array of software packages as well as by hand, with pen-and-paper. However, we have an incomplete understanding of how students' graphing practice vary by graphing environment; differences could affect how best to teach and assess…

Chemistry curriculum should account for learning in context and understanding chemistry at the macroscopic and microscopic levels: the symbol level and the process level. The "Taste of Chemistry" learning module, developed for high school chemistry majors (students who choose to study the advanced chemistry program in high school),…

In this study, we designed and developed an app called Ubiquitous-Physics (U-Physics) for mobile devices like tablet PC or smart phones to help students learn the principles behind a simple pendulum in Physics. The unique characteristic of U-Physics is the use of sensors on mobile devices to collect acceleration and velocity data during pendulum…