While much is discussed of the challenges that educators and their institutions have been facing during COVID-19, there is little reported about how students have been coping with the challenges. In this short piece, we present preliminary data on university students' perceptions of online learning and teaching during the pandemic. Our findings from a student course satisfaction survey, conducted in two universities during the 2020 summer term (June through August), reveal that students have been more resilient than is often assumed. In light of these findings as well as the reflections of authors in a previous issue of Distance Education, we will discuss some important implications for distance education scholarship.
In this work, we investigate methods for gaining greater insight from hydrological model runs conducted for uncertainty quantification and model differentiation. We frame the sensitivity analysis questions in terms of the main purposes of sensitivity analysis: parameter prioritization, trend identification, and interaction quantification. For parameter prioritization, we consider variance‐based sensitivity measures, sensitivity indices based on the L1‐norm, the Kuiper metric, and the sensitivity indices of the DELSA methods. For trend identification, we investigate insights derived from graphing the one‐way ANOVA sensitivity functions, the recently introduced CUSUNORO plots, and derivative scatterplots. For interaction quantification, we consider information delivered by variance‐based sensitivity indices. We rely on the so‐called given‐data principle, in which results from a set of model runs are used to perform a defined set of analyses. One avoids using specific designs for each insight, thus controlling the computational burden. The methodology is applied to a hydrological model of a river in Belgium simulated using the well‐established Framework for Understanding Structural Errors (FUSE) on five alternative configurations. The findings show that the integration of the chosen methods provides insights unavailable in most other analyses.
Since the beginning of the COVID-19 outbreak in Spring 2020, universities around the world have quickly adopted online teaching as an emergency measure. Informed by activity theory, the present qualitative case study aims to better understand the nature of the rapid institutional transition and its impact on academics' pedagogical experiences during this period. A multiple set of qualitative data was collected in a national university in South Korea that rapidly made the online transition, following government directives in February 2020. This article provides useful accounts of the changes that occurred in interconnected teaching activity systems at the university while adopting online teaching, highlighting the complex factors underpinning individual academics' experiences. The sudden shift in institutional teaching activities and conditions created a range of contradictions that were experienced as dilemmas by academics, the main subject of the activity systems. The results demonstrate that two groups of university faculty, separately identified as novice online teachers and expert online teachers, faced different dilemmas and challenges. An essential lesson learned from this analysis is the need for a more holistic, realistic, and sensitive approach to emergency teaching scenarios that may enable educational institutions to better respond to such emergencies in the future.
How can one make a large and complex model fast and “small”? The simulation literature has extensively addressed this problem, and the kriging method has proven to be one of the most successful methods to deal with complex simulators. In “Facing High-Dimensional Simulators: Faster Kriging?,” Xuefei Lu, Alessandro Rudi, Emanuele Borgonovo, and Lorenzo Rosasco propose a new kriging implementation, called “fast kriging,” that copes with dimensionality issues, allowing one to deal with data sets coming from simulators with thousands of inputs.
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