Student experiences of virtual reality - a case study in learning special relativity

We present a study of student learning through the use of virtual reality. A software package is used to introduce concepts of special relativity to students in a game-like environment where users experience the effects of travelling at near light speeds. From this new perspective, space and time are significantly different to that experienced in everyday life. The study explores how students have worked with this environment and how these students have used this experience in their study of special relativity. A mixed method approach has been taken to evaluate the outcomes of separate implementations of the package at two universities. Students found the simulation to be a positive learning experience and described the subject area as being less abstract after its use. Also, students were more capable of correctly answering concept questions relating to special relativity, and a small but measurable improvement was observed in the final exam

High-Tech Tools for Teaching Physics: the Physics Education Technology Project

This article appeared in the Journal of Online Teaching and Learning September 15, 2006.This paper introduces a new suite of computer simulations from the Physics Education Technology (PhET) project, identifies features of these educational tools, and demonstrates their utility. We compare the use of PhET simulations to the use of more traditional educational resources in lecture, laboratory, recitation and informal settings of introductory college physics. In each case we demonstrate that simulations are as productive, or more productive, for developing student conceptual understanding as real equipment, reading resources, or chalk-talk lectures. We further identify six key characteristic features of these simulations that begin to delineate why these are productive tools. The simulations: support an interactive approach, employ dynamic feedback, follow a constructivist approach, provide a creative workplace, make explicit otherwise inaccessible models or phenomena, and constrain students productively

Reducing model bias in a deep learning classifier using domain adversarial neural networks in the MINERvA experiment

We present a simulation-based study using deep convolutional neural networks (DCNNs) to identify neutrino interaction vertices in the MINERvA passive targets region, and illustrate the application of domain adversarial neural networks (DANNs) in this context. DANNs are designed to be trained in one domain (simulated data) but tested in a second domain (physics data) and utilize unlabeled data from the second domain so that during training only features which are unable to discriminate between the domains are promoted. MINERvA is a neutrino-nucleus scattering experiment using the NuMI beamline at Fermilab. $A$-dependent cross sections are an important part of the physics program, and these measurements require vertex finding in complicated events. To illustrate the impact of the DANN we used a modified set of simulation in place of physics data during the training of the DANN and then used the label of the modified simulation during the evaluation of the DANN. We find that deep learning based methods offer significant advantages over our prior track-based reconstruction for the task of vertex finding, and that DANNs are able to improve the performance of deep networks by leveraging available unlabeled data and by mitigating network performance degradation rooted in biases in the physics models used for training.Comment: 41 page

Oersted Medal Lecture 2007: Interactive simulations for teaching physics: What works, what doesn't, and why

We give an overview of the Physics Educational Technology (PhET) project to research and develop web-based interactive simulations for teaching and learning physics. The design philosophy, simulation development and testing process, and range of available simulations are described. The highlights of PhET research on simulation design and effectiveness in a variety of educational settings are provided. This work has shown that a well-designed interactive simulation can be an engaging and effective tool for learning physics

Validating and optimizing the effects of model progression in simulation-based inquiry learning

Model progression denotes the organization of the inquiry learning process in successive phases of increasing complexity. This study investigated the effectiveness of model progression in general, and explored the added value of either broadening or narrowing students’ possibilities to change model progression phases. Results showed that high-school students in the ‘standard’ model progression condition (n = 19), who could enter subsequent phases at will, outperformed students from a control condition (n = 30) without model progression. The unrestricted condition (n = 22) had the additional option of returning to previous phases, whereas the restricted condition (n = 20) disallowed such downward progressions as well as upward progressions in case insufficient knowledge was acquired. Both variants were found to be more effective in terms of performance than the ‘standard’ form of model progression. However, as performance in all three model progression conditions was still rather weak, additional support is needed for students to reach full understanding of the learning content