Implementation of a digital application for supporting middle-school-age learners’ thinking and conceptual growth in climate change science

Until thirty-five years ago, climate change was almost exclusively a topic of domain scientists and deeply serious hobbyists. It was only discussed in science journals and at academic colloquia. Therefore, it should not be at all surprising that many fundamental questions persist about how to teach students about climate change science, including the following. • At what age should children start to learn about climate change science? • What should be included in climate change science learning? • What are good sources of climate change science information? • What is a good starting point for teaching or learning climate change science? This dissertation addresses these and other issues, but the two overarching questions of this work are the following. • What do kids think about the science of climate change? • What are some of the reasons, scientific and otherwise, that children think the way they do about climate change science? Parents were surveyed to collect pre-participation demographic and socio-cultural information about their children, families, and communities. Such information often influences adults’ conceptualizations of climate change, but their effect on children have been very thinly researched. Selected participants were interviewed on three occasions (pre-test, post-test, and delayed-post-test) about their ideas about climate science, as well as underlying attitudes which might influence their ideas. Some of the attitudinal questions are believed to have never been asked of middle-school-age children. For one condition, participants read a compact, systems-oriented text that was developed with the help of national and international experts in climate sciences. The text represented one possible system for relating climate change science concepts to each other. Alternatively, a game in the form of a data-logging digital app was designed and developed for learners of all skill levels (including experts) of at least nine years of age. The game enabled participants to articulate their thinking about climate change concepts and compare their final responses to those from experts. The app recorded participants’ game state changes and proved to be a rich source of questions and possible explanations for their conceptual thinking. Unlike adults, the small cohort of children selected for this study exhibited few socio-cultural tendencies that often co-conspire with science misconceptions to impede climate change conceptual learning. Participants talked freely about climate science during the study, regardless of if the topic was completely new to them, which many said, or it was something familiar. With very few exceptions, they were receptive and interested to learn more about the concepts. The detail with which participants answered interview questions grew impressively with each successive interview, while the relevancy and accuracy of their responses grew modestly. In terms of children’s conceptual alignment with climate experts, there was little difference between summative results from the reading and game conditions. However, participants in the reading condition exhibited a higher degree of confidence in their thinking than participants in the game condition. Logged data from the digital app provided bases for formative assessments and analysis of conceptual cognition which were impossible with the reading. Logged event sequences provided evidence of learners’ confusion, uncertainty, organization, familiarity, and preference related to the science concepts. Two participants featured in the dissertation, Andy and Emily (not their real names), typified this range of responses. In his consecutive uses of the digital app, Andy showed a marked improvement distinguishing concepts for which he had higher confidence and agreement with the experts from those in which he had lower confidence and agreement. Emily exhibited an ability to self-monitor and integrate her responses to different methods, which resulted in her registering substantial gains between sessions. Both formal and informal educators can use such interpretations to positively affect learners’ outcomes and foster better alignment with the science

Turbulent Magnetic Field Amplification from Spiral SASI Modes: Implications for Core-Collapse Supernovae and Proto-Neutron Star Magnetization

We extend our investigation of magnetic field evolution in three-dimensional flows driven by the stationary accretion shock instability (SASI) with a suite of higher-resolution idealized models of the post-bounce core-collapse supernova environment. Our magnetohydrodynamic simulations vary in initial magnetic field strength, rotation rate, and grid resolution. Vigorous SASI-driven turbulence inside the shock amplifies magnetic fields exponentially; but while the amplified fields reduce the kinetic energy of small-scale flows, they do not seem to affect the global shock dynamics. The growth rate and final magnitude of the magnetic energy are very sensitive to grid resolution, and both are underestimated by the simulations. Nevertheless our simulations suggest that neutron star magnetic fields exceeding $10^{14}$ G can result from dynamics driven by the SASI, \emph{even for non-rotating progenitors}.Comment: 28 pages, 17 figures, accepted for publication in the Ap

Multi-Scale Data Visualization for Computational Astrophysics and Climate Dynamics

Computational astrophysics and climate dynamics are two principal application foci at the Center for Computational Sciences (CCS) at Oak Ridge National Laboratory (ORNL). We identify a dataset frontier that is shared by several SciDAC computational science domains and present an exploration of traditional production visualization techniques enhanced with new enabling research technologies such as advanced parallel occlusion culling and high resolution small multiples statistical analysis. In collaboration with our research partners, these techniques will allow the visual exploration of a new generation of peta-scale datasets that cross this data frontier along all axes