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1. Assisting K-12 Teachers to Make the Connection between Computational Thinking in Cybersecurity Unplugged Activities and Mathematical Mindset through A Cybersecurity Micro-credential

   Burrows, A. ; Borowczak, M. ; Mugayitoglu, B. ; Kennedy, C. ; Carson, A. ; Person, C. ; Finch, A. ( January 2021 , Association for the Advancement of Computing in Education (AACE))

   Langran, E. ; Archambault, L (Ed.)

   This poster focuses on K-12 STEM teachers' computational thinking while using unplugged cybersecurity activities and exploring cybersecurity applications within self-paced micro-credentials. As a result, they share the most successful and challenging unplugged cybersecurity activities. The pilot micro-credentials were created to assess their effectiveness at enabling K-12 teachers to meaningfully their own materials to develop students’ STEM readiness and increase their engagement with cybersecurity, while enabling unplugged activities to transfer to a more mathematical mindset. The two pilot studies, focusing on K-12 teacher participants, included an initial micro-credential in the 2020 summer (n=5) which informed and enhanced a second micro-credential module sequence in the fall 2020 semester (n=16). Pilot 2 consisted of a total of five modules after refining it from feedback from pilot 1, which had only 2 modules. Researchers performed a mixed-methods study, which included qualitative data collected through interviews and focus groups. Additionally, content knowledge questions and attitude surveys were also used to collect both qualitative and quantitative data. Results indicate that the majority of K-12 teachers who participated in the micro-credential pilots were able to make connections between unplugged activities and a mathematical mindset. The poster presents these findings and also includes additional resources for further exploration and inspiration. 

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2. Using Machine Learning to Generate Storm-Scale Probabilistic Guidance of Severe Weather Hazards in the Warn-on-Forecast System

   https://doi.org/10.1175/MWR-D-20-0194.1  

   Flora, Montgomery L. ; Potvin, Corey K. ; Skinner, Patrick S. ; Handler, Shawn ; McGovern, Amy ( May 2021 , Monthly Weather Review)

   Abstract A primary goal of the National Oceanic and Atmospheric Administration Warn-on-Forecast (WoF) project is to provide rapidly updating probabilistic guidance to human forecasters for short-term (e.g., 0–3 h) severe weather forecasts. Postprocessing is required to maximize the usefulness of probabilistic guidance from an ensemble of convection-allowing model forecasts. Machine learning (ML) models have become popular methods for postprocessing severe weather guidance since they can leverage numerous variables to discover useful patterns in complex datasets. In this study, we develop and evaluate a series of ML models to produce calibrated, probabilistic severe weather guidance from WoF System (WoFS) output. Our dataset includes WoFS ensemble forecasts available every 5 min out to 150 min of lead time from the 2017–19 NOAA Hazardous Weather Testbed Spring Forecasting Experiments (81 dates). Using a novel ensemble storm-track identification method, we extracted three sets of predictors from the WoFS forecasts: intrastorm state variables, near-storm environment variables, and morphological attributes of the ensemble storm tracks. We then trained random forests, gradient-boosted trees, and logistic regression algorithms to predict which WoFS 30-min ensemble storm tracks will overlap a tornado, severe hail, and/or severe wind report. To provide rigorous baselines against which to evaluate the skill of the ML models, we extracted the ensemble probabilities of hazard-relevant WoFS variables exceeding tuned thresholds from each ensemble storm track. The three ML algorithms discriminated well for all three hazards and produced more reliable probabilities than the baseline predictions. Overall, the results suggest that ML-based postprocessing of dynamical ensemble output can improve short-term, storm-scale severe weather probabilistic guidance. 

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3. A Virtual Professional Development Program for Computational Thinking During COVID-19

   Mead, H. ; Codding, D. ; Sheridan, S ; Kong, M ; Mouza, C. ; Pollock, L. ( January 2022 , Annual meeting program American Educational Research Association)

   The need to expand computer science learning for all students has led to an increase in publicly and privately funded professional development (PD) opportunities for teachers. Our research team has been involved in the design of equity-focused PD opportunities for teachers in computing since 2012 by building partnerships with K-12 systems. The COVID-19 pandemic necessitated changes in our approach and a shift to a virtual PD institute. In this work, we describe our transition to a virtual PD institute, including the topics and design principles guiding the institute. We also examine how the virtual PD influenced teacher outcomes. Findings suggest an increase in teachers’ knowledge and self-efficacy while highlighting the affordances of virtual platforms most valued by teachers. 

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4. Building a Virtual Community of Practice: Teacher Learning for Computational Thinking Infusion

   https://doi.org/10.1007/s11528-022-00729-6  

   Jocius, Robin ; O’Byrne, W. Ian ; Albert, Jennifer ; Joshi, Deepti ; Blanton, Melanie ; Robinson, Richard ; Andrews, Ashley ; Barnes, Tiffany ; Catete, Veronica ( May 2022 , TechTrends)
5. Leveraging virtual professional development to build computational thinking literacies in English language arts classrooms

   Jocius, R. ( January 2021 , Contemporary issues in technology and teacher education)

   This article describes the Infusing Computing project, a 4-year study designed to support middle and high school teachers in infusing computational thinking (CT) into their disciplinary teaching. Due to the COVID-19 pandemic, weeklong workshops held in summer 2020 were shifted to a virtual format and utilized emerging technology tools, synchronous and asynchronous sessions, explicit collaborative scaffolds, networking, and digital badging. Specifically, this study examined the experiences of English language arts (ELA) teachers (14 middle school, 13 high school) who participated in the virtual Infusing Computing workshops. Findings demonstrated that ELA teachers were able to leverage learning successfully from virtual PD to infuse CT into existing curricula, although teachers differed in the ways that they appropriated and adapted pedagogical tools for CT infusion. 

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