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Fear of negative evaluation (FNE) is the primary factor causing student anxiety in active learning. This study of 566 undergraduates establishes that LGBTQ+, first-generation, and disabled students disproportionately experience FNE, which causes students to overthink their responses and reduces their participation in class. 

Abstract The growing depth and breadth of data spanning the solar‐terrestrial environment requires new ways of representing and analyzing the available information. This paper applies one such new data representation—network analysis—to the study of Geomagnetically Induced Currents (GICs) in electric power lines. This work uses newly available electric current data collected by power utilities through the the Electric Power Research Institute (EPRI) SUNBURST project and magnetometer data from the Super Magnetometer Initiative. The magnetometer data are analyzed using wavelet analysis. This new analysis method shows deviations to be more likely for equatorial stations close to water, which may be caused by the coast effect. The deviation likelihood is a complex function of latitude and magnetic local time. The GIC data are analyzed using “Quiet Day Curves” (QDCs) which help isolate geomagnetic disturbances. We find that current deviations are more common in the early morning sector, but this trend differs from station to station. These current and magnetometer data are represented in a network as nodes which are connected when both the current and magnetic measurements have a statistically significant deviation from their baseline behavior. This network is used to study the link between space weather and GICs. To do this, times when a current deviation exists are compared to times when magnetic deviations exist for each magnetometer ‐ current sensor pair. Current deviations are, on average, 1.83 times more likely when there are magnetic deviations. However, some magnetometer deviations are more indicative than others, with the strongest probability multipliers reaching 3. 

Abstract Geomagnetically induced currents (GICs) at middle latitudes have received increased attention after reported power grid disruptions due to geomagnetic disturbances. However, quantifying the risk to the electric power grid at middle latitudes is difficult without understanding how the GIC sensors respond to geomagnetic activity on a daily basis. Therefore, in this study the question “Do measured GICs have distinguishable and quantifiable long‐period and short‐period characteristics?” is addressed. The study focuses on the long‐term variability of measured GIC, and establishes the extent to which the variability relates to quiet‐time geomagnetic activity. GIC quiet‐day curves (QDCs) are computed from measured data for each GIC node, covering all four seasons, and then compared with the seasonal variability of thermosphere‐ionosphere‐electrodynamics general circulation model (TIE‐GCM)‐simulated neutral wind and height‐integrated current density. The results show strong evidence that the middle‐latitude nodes routinely respond to the tidal‐driven Sq variation, with a local time and seasonal dependence on the direction of the ionospheric currents, which is specific to each node. The strong dependence of GICs on the Sq currents demonstrates that the GIC QDCs may be employed as a robust baseline from which to quantify the significance of GICs during geomagnetically active times and to isolate those variations to study independently. The QDC‐based significance score computed in this study provides power utilities with a node‐specific measure of the geomagnetic significance of a given GIC observation. Finally, this study shows that the power grid acts as a giant sensor that may detect ionospheric current systems.