Older Adults Using Technology for Meaningful Activities During COVID-19: An Analysis Through the Lens of Self-Determination Theory

Restrictions during the COVID-19 pandemic significantly affected people's opportunities to engage in activities that are meaningful to their lives. In response to these constraints, many people, including older adults, turned to digital technologies as alternative ways to pursue meaningful activities. These technology-mediated activities, however, presented new challenges for older adults' everyday use of technology. In this paper, we investigate how older adults used digital technologies for meaningful activities during COVID-19 restrictions. We conducted in-depth interviews with 40 older adults and analyzed the interview data through the lens of self-determination theory (SDT). Our analysis shows that using digital technologies for meaningful activities can both support and undermine older people's three basic psychological needs for autonomy, competence, and relatedness. We argue that future technologies should be designed to empower older adults' content creation, engagement in personal interests, exploration of technology, effortful communication, and participation in beneficent activities. © 2023 ACM.

Through the Lens of Students: MS Teams as a Sustainable Pedagogical Tool

The COVID-19 pandemic has highlighted the need for higher education institutions to modernize and embrace the post-digital age. This study evaluates students' perspectives of utilizing MS Teams as a means of facilitating remote learning during the pandemic. The Technology Acceptance Model (TAM) was employed as the theoretical framework to examine students' views on self-efficacy, facilitating conditions, ease of use, usefulness, and intention to use. The results showcase positive views of MS Teams, with self-efficacy rated the highest among the five constructs, followed by ease of use, facilitating conditions, intention to use, and usefulness. Additionally, no significant differences were found in students' perceptions based on gender. MS Teams has proven to be a successful platform for delivering online learning and communicating, bridging the divide of distance and time in teaching and learning. As discussions about the future of higher education in the post-pandemic world have commenced among academia and university officials, it is crucial to consider the impact of COVID-19 on student learning and provide suggestions for a more sustainable and effective post-pandemic education. © 2023 IEEE.

Engineering in a Crisis: Observing Students' Perceptions of Macroethical Responsibilities during Pandemics and Natural Disasters

When the ethical responsibilities of engineers are discussed in classrooms, the focus is usually on microethics, which concentrates on individual decision-making, rather than macroethics, that addresses broad societal concerns. Pandemics (e.g., COVID-19) and natural disasters (e.g., hurricanes, derechos) have presented unique opportunities to observe engineering macroethical responsibilities, because unjust social, economic, and environmental systems have been brought to the forefront amidst the responses (e.g., inequitable transportation access). In this paper, we consider pandemics and natural disasters through the lens of engineering macroethics, aiming to understand students' perceptions about the macroethical responsibilities of engineers. In the fall of 2020, we deployed a survey to undergraduate engineering students at two universities (n=424). Students were asked to discuss what they perceived to be the role of engineering professionals in response to the global COVID-19 pandemic and natural disasters. We used a qualitative content analysis to explore the macroethical responsibilities mentioned in students' responses. Many of these responses include considerations of infrastructure resilience, resource distribution, and community equity. Logit models were used to identify which sociodemographic factors were associated with responses that included macroethical responsibilities, revealing engineering major (specifically, civil engineering), employment status, gender identity, and family size, among others as significant factors. The implications from this study include recommendations on curricular content, and identifying which student sociodemographic groups would especially benefit from macroethical content in coursework. © 2023 American Society of Civil Engineers.

Online Distance Instruction in Higher Education Through the Lens of Students' Experience

The main objective of the research is to monitor online distance instruction at the higher education level and to discover whether there were any changes in the process within the period of 18-month period, when schools were closed due to the covid-19 pandemic. The ex-post-facto method was applied in the research. Data were collected by questionnaire. In total, almost 300 (resp. 642) respondents from three institutions participated in the research. Four statements were under focus: (1) Teachers invested much effort in online distance teaching;(2) Students invested much effort in online distance learning;(3) I appreciate online distance instruction;(4) I did not learn much through online distance instruction. Respondents' dis/agreement with statements was considered on a four-level Likert. A quantitative approach was used to calculate the frequency of occurrence of responses in single statements. We anticipated that there will be both supporters and rejectors of online distance instruction. Particular expectations are reflected in the responses to the statements. However, the results are limited by the convenience of the sample and also/mainly by the ability and willingness of the students to consider their learning objectively, to confess whether they are able to learn autonomously, in particular, whether they have sufficient level of inner motivation to learn. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Deep Graph Networks for Drug Repurposing with Multi-Protein Targets

In the early phases of the COVID-19 pandemic, repurposing of drugs approved for use in other diseases helped counteract the aggressiveness of the virus. Therefore, the availability of effective and flexible methodologies to speed up and prioritize the repurposing process is fundamental to tackle present and future challenges to worldwide health. This work addresses the problem of drug repurposing through the lens of deep learning for graphs, by designing an architecture that exploits both structural and biological information to propose a reduced set of drugs that may be effective against an unknown disease. Our main contribution is a method to repurpose a drug against multiple proteins, rather than the most common single-drug/single-protein setting. The method leverages graph embeddings to encode the relevant proteins'and drugs'information based on gene ontology data and structural similarities. Finally, we publicly release a comprehensive and unified data repository for graph-based analysis to foster further studies on COVID-19 and drug repurposing. We empirically validate the proposed approach in a general drug repurposing setting, showing that it generalizes better than single protein repurposing schemes. We conclude the manuscript with an exemplified application of our method to the COVID-19 use case. All source code is publicly available. IEEE

"WORKSHOP ASSISTANTS: THE SECRET SAUCE OF VM WORKSHOPS”

In the advent of COVID, the value industry has found a new way of conducting value studies in the virtual environment which presents opportunities for developing innovative approaches, new skillsets, new tools, and consequently new and/or expanding roles and responsibilities for participants. As part of this adaptation, the role of Workshop Assistant has changed and taken on a more important role in value studies than ever before. The Workshop Assistant today serves several functions-administrative, technical and co-facilitation-to provide immense value (function/resources) during all stages of a value study. This paper focuses on the role of Workshop Assistant as featured through the lens of SAVE International's Core Competencies with the nuances for both in-person and virtual value studies highlighted. The Workshop Assistant supports a group to improve how it identifies and solves problems, supports the CVS Facilitator, and increases the group's effectiveness. © SAVE International 2022 Value Summit Proceedings: Turning Up the Heat on Value. All Rights Reserved.

CovidLens: Visually Understanding the Covid-19 Indicators through the Lens of Mobility Data

Since the onset of the Covid-19 pandemic, an over-whelming amount of related data has been released. In an attempt to gain insights from that data, multiple public data visualization dashboards have been deployed. Differently from such dashboards, which mainly support basic data filtering and visualization of separate datasets, in this work, we propose CovidLens, which: 1) integrates various Covid-19 indicators and is centred around the Google Community Mobility Report dataset, 2) supports similarity search for finding similar and correlated patterns and trends across the integrated datasets, and 3) automatically recommends insightful visualizations that unlocks valuable insights into the pandemic effects. To that end, we will be presenting the employed dataset, together with the design, implementation, and multiple usage scenarios of our proposed CovidLens. © 2022 IEEE.

A Tale of Two Countries: A Longitudinal Cross-Country Study of Mobile Users' Reactions to the COVID-19 Pandemic Through the Lens of App Popularity

The ongoing COVID-19 pandemic has profoundly impacted people's lives around the world, including how they interact with mobile technologies. In this paper, we seek to develop an understanding of how the dynamic trajectory of a pandemic shapes mobile phone users' experiences. Through the lens of app popularity, we approach this goal from a cross-country perspective. We compile a dataset consisting of six-month daily snapshots of the most popular apps in the iOS App Store in China and the US, where the pandemic has exhibited distinct trajectories. Using this longitudinal dataset, our analysis provides detailed patterns of app ranking during the pandemic at both category and individual app levels. We reveal that app categories' rankings are correlated with the pandemic, contingent upon country-specific development trajectories. Our work offers rich insights into how the COVID-19, a typical global public health crisis, has influence people's day-to-day interaction with the Internet and mobile technologies. © 2022 ACM.

COVID Student Study: A Year in the Life of College Students during the COVID-19 Pandemic Through the Lens of Mobile Phone Sensing

The COVID-19 pandemic continues to affect the daily life of college students, impacting their social life, education, stress levels and overall mental well-being. We study and assess behavioral changes of N=180 undergraduate college students one year prior to the pandemic as a baseline and then during the first year of the pandemic using mobile phone sensing and behavioral inference. We observe that certain groups of students experience the pandemic very differently. Furthermore, we explore the association of self-reported COVID-19 concern with students' behavior and mental health. We find that heightened COVID-19 concern is correlated with increased depression, anxiety and stress. We evaluate the performance of different deep learning models to classify student COVID-19 concerns with an AUROC and F1 score of 0.70 and 0.71, respectively. Our study spans a two-year period and provides a number of important insights into the life of college students during this period. © 2022 Owner/Author.

Racism is a virus: Anti-asian hate and counterspeech in social media during the COVID-19 crisis@gatech.edu

The spread of COVID-19 has sparked racism and hate on social media targeted towards Asian communities. However, little is known about how racial hate spreads during a pandemic and the role of counterspeech in mitigating this spread. In this work, we study the evolution and spread of anti-Asian hate speech through the lens of Twitter. We create COVID-HATE, the largest dataset of anti-Asian hate and counterspeech spanning 14 months, containing over 206 million tweets, and a social network with over 127 million nodes. By creating a novel hand-labeled dataset of 3,355 tweets, we train a text classifier to identify hateful and counterspeech tweets that achieves an average macro-F1 score of 0.832. Using this dataset, we conduct longitudinal analysis of tweets and users. Analysis of the social network reveals that hateful and counterspeech users interact and engage extensively with one another, instead of living in isolated polarized communities. We find that nodes were highly likely to become hateful after being exposed to hateful content in the year 2020. Notably, counterspeech messages discourage users from turning hateful, potentially suggesting a solution to curb hate on web and social media platforms. Data and code is available at http://claws.cc.gatech.edu/covid. © 2021 ACM.

Conversations around organizational risk and insider threat

Organizational risk and resilience as well as insider threat have been studied through the lenses of socio-psychological studies and information and computer sciences. As with all disciplines, it is an area in which practitioners, enthusiasts, and experts discuss the theory, issues, and solutions of the field in various online public forums. Such conversations, despite their public nature, can be difficult to understand and to study, even by those deeply involved in the communities themselves. Who are the key actors? How can we understand and characterize the culture around such communities, the problems they face, and the solutions favored by the experts in the field? Which narratives are being created and propagated, and by whom - and are these actors truly people, or are they autonomous agents, or "bots"? In this paper, we demonstrate the value in applying dynamic network analysis and social network analysis to gain situational awareness of the public conversation around insider threat, nation-state espionage, and industrial espionage. Characterizing public discourse around a topic can reveal individuals and organizations attempting to push or shape narratives in ways that might not be obvious to casual observation. Such techniques have been used to great effect in the study of elections, the COVID-19 pandemic, and the study of misinformation and disinformation, and we hope to show that their use in this area is a powerful way to build a foundation of understanding around the conversations in the online public forum, provide data and analysis for use in further research, and equip counter insider threat practitioners with new insights. © 2021 Owner/Author.

THE COVID-19 PANDEMIC AND PSYCHOLOGICAL CONTRACT BREACH: THE DISRUPTION OF A WORK FROM HOME SCHEDULE

The COVID-19 Pandemic created a chain reaction that fundamentally altered many employees' day-to-day lives. In this study, we observe the impact of this shock on the breach of the employees' psychological contract. Through the lens of affective event theory, we test the research model using survey data from 239 employees who worked from home during the pandemic. Findings suggest that moving to a work from home schedule undermines the trust between the employees and employers, which, in turn, leads to an increase of work-life conflict for those working from home and promotes recognition of a breach of psychological contract. © AoM 2021.All right reserved.