ABSTRACT
This research paper presents a group project framework for a second-year programming course, which was conducted during the COVID-19 pandemic. The framework offers well defined stages of the group project which allow students to work on their choice of a real-world problem, integrate their learnings from previous courses, and present a working solution. In the group project, students actively participate, reflect, and contribute to achieving the goals set in the learning objectives of the course. Our framework incorporates key features from Kolb's Experiential Learning Theory (1984) and principles of active learning from Barnes (1989) to achieve active and experiential learning through active supervision. The use of group projects as a teaching pedagogy is widely adopted in many universities. Students work together, develop a plan, and demonstrate their abilities in building on existing knowledge acquired from previous courses, and apply them appropriately for problem solving. Prior to the pandemic, it was the norm for students to work on their group projects together by meeting physically on campus. Key benefits of working together physically are having the support of one another and the ease of communication. With the onset of the pandemic, safe distancing measures, and restrictions put in place have made it challenging for students to work on group projects together. During the pandemic, many courses were forced to move online with limited face-to-face learning opportunities on campus. This posed great challenges to the faculty in terms of effective supervision of students and their project progress. To mitigate the challenges, we devised a flexible strategy that makes use of both technology-based and non-technological means for monitoring students' group project milestones. The faculty receives continuous updates from students as they work towards each milestone. These milestones serve as important checkpoints for students. Continuous checks at different milestones help the faculty adopt appropriate intervention measures as issues arise. The group project learning framework consists of three main stages, namely Group Formation, Scoping of the Project, and Group Solutioning. The framework is overlaid with Kolb's Experiential Learning Theory concepts to describe the learnings, milestones, and deliverables of each stage. Each of these stages adopts Barnes's principles of active learning to enable active participation, reflection, and contribution by students. We evaluated the success of this framework through a comprehensive student survey analysis. The survey asked specific questions to students on all stages of the group project and the overarching component of teamwork and working online. We also present our findings and lessons learned for improvements of the framework. We believe that our framework will be valuable to educators in computing programs that wish to adopt effective supervision measures for group projects.
ABSTRACT
Background. From March 2020 through May 2021, Dallas County reported a total of 304,056 cases of COVID-19, including 4,073 deaths. During the month of December 2020, a post-holiday surge of cases led to peak daily average case rates of over 50 cases per 100,000. COVID-19 cases and deaths have since declined substantially following the rollout of COVID-19 vaccine delivery. As of June 8, 2021, about 1,831,588 Dallas County residents have received at least one COVID-19 vaccine dose and 910,067 are fully vaccinated. Recent county integration of immunization and case databases enabled identification and analysis of COVID-19 breakthrough infections. Methods. A COVID-19 breakthrough infection was defined as a positive test (PCR or antigen) collected from an individual ≥ 14 days after receiving the full series of an FDA-authorized COVID-19 vaccine. Nationally, 10,262 vaccine breakthrough infections had been reported from 46 US states and territories, through April 2021. Vaccine breakthrough cases were reviewed and medical records ed to collect demographic information, clinical characteristics, and medical conditions. Data analysis was performed using R, version 4.0.2 (2020). Results. Of the 700 vaccine breakthrough cases reported in Dallas County residents as of June 8, 2021, 304 (43%) were male and 396 (57%) female, with an average age of 53 years. The majority of the vaccine breakthrough cases were White (42%);25% were Hispanic/Latino;and 20% were Black. Almost all breakthrough cases were confirmed with PCR testing, with 451 (64%) cases receiving the Pfizer vaccine. Of breakthrough cases, 49% were symptomatic;52% (358) had underlying conditions including: tobacco use, obesity, or immunocompromised state;68 (10%) were hospitalized;and 11 (1.6%) died. Whole genome sequencing was performed on 51 cases, with 14 (27.5%) variants identified, including: eight B.1.1.7, two B.1.429 and one P.1 variants. Conclusion. Despite the high levels of vaccine efficacy documented in US vaccine trials, COVID-19 breakthrough infections, though currently uncommon, do occur and are important to investigate. Ongoing close public health surveillance of variants is needed to discern changes in patterns of vaccine efficacy and characteristics of populations at greatest risk of severe disease from COVID-19.
ABSTRACT
Background. During 2020, a total of 193,318 cases of COVID-19 were reported in Dallas, with daily average case rates exceeding 50 per 100,000 for over 7 weeks. An adaptable survey functionality within a newly implemented COVID-19 surveillance system provided an opportunity to assess case knowledge and attitudes about isolation and contact tracing efforts. Methods. COVID-19 illnesses were classified using the 2020 CSTE case definitions. Cases were interviewed and records reviewed for exposures and illness characteristics. Supplemental questionnaires assessing knowledge of public health recommendations were given to a convenience sample of 987 cases during the month of December 2020. Fishers exact and chi-square analyses were performed using SAS 9.4. Results. Of the 987 respondents, 99% reported beginning isolation on or before receipt of test results, and 1% were not in isolation at the time of public health interview. Of cases reporting contacts, 92% had advised household members to quarantine prior to interview, and 91% did not want public health to call their household. Of cases reporting non-household close contacts, 75% had advised these contacts to quarantine prior to interview, and 91.3% did not want the health department to call these persons. Cases ≥ 65 years were less likely to have notified their own close contacts (OR: 0.2;95% CI=0.1-0.8) of their test results, and more likely to prefer the health department to notify their household contacts of their positive result (OR: 4.1;95% CI=1.3-12.5). Compared with White cases, Hispanic cases were less likely to be aware that their test was positive at the time of interview (OR: 0.3;95% CI=0.1-0.7). Non-White cases were less likely to be aware of resources for food, rent and utility assistance prior to interview (OR: 0.25;95% CI=0.1-0.7). All respondents perceived the public health interview to have been of some value to them, most often to answer their questions about retesting (51%) and duration of isolation (48%). Conclusion. The aversion of a majority of COVID-19 cases for health department notification of their contacts is a significant deterrent to name-based contact tracing approaches. Acknowledgement of this limitation could better focus existing resources on the delivery of expedited notifications and information to contacts by proxy.
ABSTRACT
The COVID-19 pandemic triggered a large-scale work-from-home trend globally in recent months. In this paper, we study the phenomenon of "work-from-home" (WFH) by performing social listening. We propose an analytics pipeline designed to crawl social media data and perform text mining analyzes on textual data from tweets scrapped based on hashtags related to WFH in COVID-19 situation. We apply text mining and NLP techniques to analyze the tweets for extracting the WFH themes and sentiments (positive and negative). Our Twitter theme analysis adds further value by summarizing the common key topics, allowing employers to gain more insights on areas of employee concerns due to pandemic.
ABSTRACT
Our study presents a comprehensive analysis of news articles from FlightGlobal website during the first half of 2020. Our analyses reveal useful insights on themes and trends concerning the aviation industry during the COVID-19 period. We applied text mining and NLP techniques to analyse the articles for extracting the aviation themes and article sentiments (positive and negative). Our results show that there is a variation in the sentiment trends for themes aligned with the real-world developments of the pandemic. The article sentiment analysis can offer industry players a quick sense of the nature of developments in the industry. Our article theme analysis adds further value by summarizing the common key topics within the positive and negative corpora, allowing stakeholders in the aviation industry to gain more insights on areas of concerns or aspects that are affected by the pandemic. © 2020 IEEE.