ABSTRACT
We propose Computational Thinking (CT) as an innovative pedagogical approach with broad application. Research and current industry trends illustrate that students should have a solid computational thinking ability in order to have the skills required for future jobs in Artificial Intelligence. Due to current social issues regarding COVID-19 and natural disasters, we are rapidly moving towards a cyberspace era where many citizens will conduct their work online. Understanding the foundations and tools of computation - e.g., ion, decomposition, pattern recognition - is critical for any student to be prepared for the digital AI age. Believing students should be fully prepared for future jobs that involve computation, we developed a CT module on a Learning Management System (LMS). We have collected data of students who took our CT course module. We looked into the students' activity records and analyzed the number of students' views on the pages and the number of participants on each quiz. We counted the total number of engagements of the ten components in the CT course module. Ultimately, we believe that our modules had a greater impact on those students who were newer to computational thinking, over those who had prior experience and were enrolled in upper-level computational courses. © American Society for Engineering Education, 2021
ABSTRACT
We propose Computational Thinking (CT) as an innovative pedagogical approach with broad application. Research and current industry trends illustrate that students should have a solid computational thinking ability in order to have the skills required for future jobs in Artificial Intelligence. Due to current social issues regarding COVID-19 and natural disasters, we are rapidly moving towards a cyberspace era where many citizens will conduct their work online. Understanding the foundations and tools of computation - e.g., ion, decomposition, pattern recognition - is critical for any student to be prepared for the digital AI age. Believing students should be fully prepared for future jobs that involve computation, we developed a CT module on a Learning Management System (LMS). We have collected data of students who took our CT course module. We looked into the students' activity records and analyzed the number of students' views on the pages and the number of participants on each quiz. We counted the total number of engagements of the ten components in the CT course module. Ultimately, we believe that our modules had a greater impact on those students who were newer to computational thinking, over those who had prior experience and were enrolled in upper-level computational courses. © American Society for Engineering Education, 2021
ABSTRACT
This paper explores the impact of COVID-19 on 911 Call behavior to help first responders develop effective solutions to emergent situations proactively. Correct prediction of call volume and call type helps first responders optimize resource allocation. We used time series regression to explore the relationship between the number of COVID-19 cases, weather, and stay-at-home orders using 911 Call records in New Hanover County, North Carolina, USA. We divided 911 calls into six categories: breathing, domestic violence, injury, psychiatric, traffic, and violence-related calls. We observed a positive correlation between the number of COVID-19 cases and the number of 911 calls in all categories except domestic violence. We also developed a Bayesian regression prediction model to forecast the number of 911 calls given the number of COVID-19 cases. Our model excelled regarding domestic violence and total calls, and achieved satisfactory results for traffic and violence calls. To our knowledge, there is no prior relevant work, so we were unable to compare our results with other models. © 2021 ACM.