Thriving in a Pandemic: Lessons Learned from a Resilient University Program Seen Through the CoI Lens (preprint)

In March 2020, college campuses underwent a sudden transformation to online learning due to the COVID-19 outbreak. To understand the impact of COVID-19 on students' expectations, this study conducted a three-year survey from ten core courses within the Project Management Center for Excellence at the University of Maryland. The study involved two main steps: 1) a statistical analysis to evaluate students' expectations regarding "student," "class," "instructor," and "effort;" and 2) a lexical salience-valence analysis (LSVA) through the lens of the Community of Inquiry (CoI) framework to show the changes of students' expectations. The results revealed that students' overall evaluations maintained relatively consistent amid the COVID-19 teaching period. However, there were significant shifts of the student expectations toward Cognitive, Social and Teaching Presence course elements based on LSVA results. Also, clear differences emerged between under-graduates and graduates in their expectations and preferences in course design and delivery. These insights provide practical recommendations for course instructors in designing effective online courses.

Can Social Media Data Be Utilized to Enhance Early Warning: Retrospective Analysis of the U.S. Covid-19 Pandemic (preprint)

ABSTRACT The U.S. needs early warning systems to help it contain the spread of infectious diseases. Conventional early warning systems use lab-test results or dynamic records to signal early warning signs. New early warning systems can supplement these data with indicators of public awareness like news articles and search queries. This study aims to explore the potential of utilizing social media data to enhance early warning of the COVID-19 outbreak. To demonstrate the feasibility, this study conducts a retrospective analysis and investigates more than 14 million related Twitter postings in the date range from January 20 to March 10, 2020. With the aid of natural language processing tools and machine learning classifiers, this study classifies each of these tweets into either a signal or a non-signal. In this study, a “signal” tweet implies that the user recognized the COVID-19 outbreak risk in the U.S. This study then proposes a parameter “signal ratio” to signal warning signs of the COVID-19 pandemic over periods. Results reveal that social media data and the signal ratio can detect the hazards ahead of the COVID-19 outbreak. This claim has been validated with a leading time of 16 days through the comparison to other referenced methods based on Google trends or media news.