ABSTRACT
The COVID-19 pandemic has killed millions and gravely disrupted the world's economy. A safe and effective vaccine was developed remarkably swiftly, but as of yet, uptake of the vaccine has been slow. This paper explores one potential explanation of delayed adoption of the vaccine, which is data privacy concerns. We explore two contrasting regulations that vary across U.S. states that have the potential to affect the perceived privacy risk associated with receiving a COVID-19 vaccine. The first regulation—an "identification requirement”—increases privacy concerns by requiring individuals to verify residency with government approved documentation. The second regulation—"anonymity protection”—reduces privacy concerns by allowing individuals to remove personally identifying information from state-operated immunization registry systems. We investigate the effects of these privacy-reducing and privacy-protecting regulations on U.S. state-level COVID-19 vaccination rates. Using a panel data set, we find that identification requirements decrease vaccine demand but that this negative effect is offset when individuals are able to remove information from an immunization registry. Our results remain consistent when controlling for CDC-defined barriers to vaccination, levels of misinformation, vaccine incentives, and states' phased distribution of vaccine supply. These findings yield significant theoretical and practical contributions for privacy policy and public health. © 2022 INFORMS.
ABSTRACT
Has the COVID pandemic had noticeable effects on retention of students in computing disciplines? Are those not retained leaving academia in different ways than they did in pre-COVID years? Are program graduates staying in academia at the next degree level similarly to preCOVID times? Tis study by the ACM Retention Committee explores the answers to these questions with respect to U.S. students in bachelor's and associate's level degree programs. Data from the academic years immediately preceding COVID (2018-19, 2019-20), is compared with that from academic years 2020-21 and 2021-22. © This work is licensed under a Creative Commons Attribution-Share Alike International 4.0 License.
ABSTRACT
The COVID-19 pandemic has killed millions and gravely disrupted the world's economy. A safe and effective vaccine was developed remarkably swiftly, but as of yet, uptake of the vaccine has been slow. This paper explores one potential explanation of delayed adoption of the vaccine, which is data privacy concerns. We explore two contrasting regulations that vary across U.S. states that have the potential to affect the perceived privacy risk associated with receiving a COVID-19 vaccine. The first regulation-an "identification requirement"-increases privacy concerns by requiring individuals to verify residency with government approved documentation. The second regulation-"anonymity protection"- reduces privacy concerns by allowing individuals to remove personally identifying information from state-operated immunization registry systems. We investigate the effects of these privacy-reducing and privacy-protecting regulations on U.S. state-level COVID-19 vaccination rates. Using a panel data set, we find that identification requirements decrease vaccine demand but that this negative effect is offset when individuals are able to remove information from an immunization registry. Our results remain consistent when controlling for CDC-defined barriers to vaccination, levels of misinformation, vaccine incentives, and states' phased distribution of vaccine supply. These findings yield significant theoretical and practical contributions for privacy policy and public health.
ABSTRACT
This paper presents an investigation of the effectiveness of the connected learning and integrated course knowledge (CLICK) approach. The CLICK approach aims to integrate the knowledge across the industrial engineering (IE) curriculum by leveraging immersive technology, i.e., 3D simulation and virtual reality (VR). The effectiveness of the CLICK approach is measured by its impact on students' motivation, engineering identity, and learning outcomes. In this work, a virtual system that simulates a manufacturing assembly system was developed and used in an operations research (OR) course. The virtual system includes data collection tasks and exercises to calculate statistics that are taught in a probability and statistics course, and inventory and queueing theories concepts that are taught in an operations research course. The virtual system (CLICK learning module) is used to teach inventory and queueing theory concepts. Due to COVID-19 and the sudden shift to remote learning, the research team faced challenges including limitations in performing in-person experiments on campus as well as the potential risk of spreading the disease when VR headsets are used by several people. To alleviate some of the challenges, the researchers built the virtual system in simulation software, i.e., Simio, to provide more flexibility and scalability. The virtual system can be run on a regular personal computer without the need for a VR-ready computer and VR headsets. Yet, the virtual system can be run on an Oculus VR headset if the student prefers to do so. The study involves two groups: Control and intervention groups. The control group is represented by the students who are taught traditionally while the intervention group is represented by the students who are taught with the aid of the CLICK learning module. The results of this study compared the groups in terms of students' motivation, and engineering identity. The learning outcomes were assessed using a self-assessment instrument and the student's grades in the learning module. The data of the control and intervention groups were collected at Penn State Behrend in Fall 2019, and Fall 2020 semesters, respectively. The groups were not statistically significantly different for motivation and Engineering Identity, however, the resulted motivation and Engineering Identity scores for the intervention group were not worse than the control group considering the shift to remote learning setting. The students showed good learning outcomes when the CLICK learning module was used. The grades were positively correlated to the motivation and Engineering Identity scores. © American Society for Engineering Education, 2021
ABSTRACT
Artificial intelligence (AI) techniques such as Generative Neural Networks (GNNs) have resulted in remarkable breakthroughs such as the generation of hyper-realistic images, 3D geometries, and textual data. This work investigates the vulnerability of science, technology, engineering, and mathematics (STEM) learners to AI-generated misinformation in order to safeguard the public-availability of high-quality online STEM learning content. The COVID-19 pandemic has increased STEM learners' reliance on online learning content. Consequently, safeguarding the veracity of STEM learning content is critical to ensuring the safety and trust that both STEM educators and learners have in publicly-available STEM learning content. In this study, state-of-the-art AI algorithms are trained on a specific STEM context (i.e., climate change) using publicly-available data. STEM learners are then randomly presented with authentic and AI-manipulated STEM learning content and asked to judge the authenticity of the content. The authors introduce an approach that STEM educators can employ to understand correlations between STEM learning topics such as climate change, and students' susceptibility to AI-driven misinformation. The proposed approach has the potential to guide STEM educators as to the STEM topics that may be more difficult to teach (e.g., climate change), given students' susceptibility to AI-driven misinformation that promotes controversial viewpoints. In addition, the proposed approach may inform students themselves as to their susceptibility to AI-driven STEM misinformation so that they are more aware of AI's capabilities and how they could be utilized to alter their viewpoints on a STEM topic. © American Society for Engineering Education, 2021
ABSTRACT
This paper measures the role of the diffusion of high-speed Internet on an individual's ability to self-isolate during a global pandemic. We use data that tracks 20 million mobile devices and their movements across physical locations, and whether the mobile devices leave their homes that day. We show that while income is correlated with differences in the ability to stay at home, the unequal diffusion of high-speed Internet in homes across regions drives much of this observed income effect. We examine compliance with state-level directives to avoid leaving your home. Devices in regions with either high-income or high-speed Internet are less likely to leave their homes after such a directive. However, the combination of having both high income and highspeed Internet appears to be the biggest driver of propensity to stay at home. Our results suggest that the digital divide---or the fact that income and home Internet access are correlated---appears to explain much inequality we observe in people's ability to self-isolate.
ABSTRACT
The Mexico-UK Sub-Millimetre Camera for AsTronomy (MUSCAT) is a 1.1-mm band receiver consisting of 1,500 single-colour lumped-element kinetic inductance detectors and is scheduled for deployment to the Large Millimeter Telescope (LMT) after the ongoing COVID-19 pandemic. MUSCAT is designed to utilise the full field of view of the LMT's upgraded 50-m primary mirror (approximately 4'). Here we will present the as-measured performance of MUSCAT from the final lab-verification testing prior to shipping to the LMT. We will also explain the overall design of MUSCAT including the novel technologies utilised-such as continuous cooling using sorption coolers and a miniature dilutor, and horn-coupled LEKIDs-for which MUSCAT will provide a first on-sky demonstration.