Forecasting COVID-19 Vaccination Rates using Social Media Data

The COVID-19 pandemic has had a profound impact on the global community, and vaccination has been recognized as a crucial intervention. To gain insight into public perceptions of COVID-19 vaccines, survey studies and the analysis of social media platforms have been conducted. However, existing methods lack consideration of individual vaccination intentions or status and the relationship between public perceptions and actual vaccine uptake. To address these limitations, this study proposes a text classification approach to identify tweets indicating a user's intent or status on vaccination. A comparative analysis between the proportions of tweets from different categories and real-world vaccination data reveals notable alignment, suggesting that tweets may serve as a precursor to actual vaccination status. Further, regression analysis and time series forecasting were performed to explore the potential of tweet data, demonstrating the significance of incorporating tweet data in predicting future vaccination status. Finally, clustering was applied to the tweet sets with positive and negative labels to gain insights into underlying focuses of each stance. © 2023 ACM.

Students' well-being and factors impacting it during COVID-19 pandemic–early findings from Delft University of Technology

Even before the COVID-19 pandemic, student well-being was highlighted as an important public health issue. The study aims to gain insights into the exact factors that bachelor and master students from engineering fields at Delft University of Technology are impacted by. Multiple interviews were performed to identify the key areas of impact and then incorporated into a comprehensive survey. The questionnaire was divided into five blocks: course work factors, thesis, communication, study environment, the COVID-19 pandemic and disseminated between June and September of 2021. A convenience sample of 165 responses was collected and the Warwick-Edinburgh Mental Well-being Scale (WEMWBS) test was employed to quantify the well-being of the students. The survey analysis found different well-being scores between the students from the bachelor and master programs and concluded that having a consistent work environment played an important role in students' welfare. The COVID-19-related findings revealed that the recordings of lectures and remote studying were the most appreciated. The thesis-related section showed that the clarity and objectives of the thesis writing are particularly impactful. Although some of the findings are university specific, the recommendations could be considered by other universities as they refer to general indicators and relationships. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

COVID-19 Dataset Clustering based on K-Means and EM Algorithms

In this paper, a COVID-19 dataset is analyzed using a combination of K-Means and Expectation-Maximization (EM) algorithms to cluster the data. The purpose of this method is to gain insight into and interpret the various components of the data. The study focuses on tracking the evolution of confirmed, death, and recovered cases from March to October 2020, using a two-dimensional dataset approach. K-Means is used to group the data into three categories: "Confirmed-Recovered”, "Confirmed-Death”, and "Recovered-Death”, and each category is modeled using a bivariate Gaussian density. The optimal value for k, which represents the number of groups, is determined using the Elbow method. The results indicate that the clusters generated by K-Means provide limited information, whereas the EM algorithm reveals the correlation between "Confirmed-Recovered”, "Confirmed-Death”, and "Recovered-Death”. The advantages of using the EM algorithm include stability in computation and improved clustering through the Gaussian Mixture Model (GMM). © 2023,International Journal of Advanced Computer Science and Applications. All Rights Reserved.

An Exploration of a Novice Kindergarten Teacher's Enactment of Multiliteracies Pedagogy during the Pandemic: A Case Study of a Virtual Kindergarten Classroom

We aim to gain insight into technology-enhanced literacy learning for kindergarten students during the COVID-19 pandemic by exploring a novice kindergarten teacher's practice of multiliteracies pedagogy in his virtual kindergarten classroom. This qualitative case study collected data from multiple sources such as virtual interviews and classroom observations, the Kindergarten Program (KP) document, teacher's reflective notes, lesson plans, students' artefacts, and researchers' observational notes and reflective journals. This study found that although the novice kindergarten teacher provided various multimodal learning opportunities for students, his literacy practice emphasized phonological awareness, phonemic awareness, and letter-sound correspondence. Also, he faced numerous challenges due to inadequate teacher preparation and professional development, inconsistency of the quality and utility of technology, constraints of virtual learning for young learners, varying degrees of parental support, and challenges of implementing multiliteracies pedagogy with young children virtually. This study contributes to the existing literature on online learning for kindergarten students and expands the burgeoning multiliteracies research from physical to virtual learning environments. Also, this study demonstrates how virtual learning opens up opportunities to advance the multiliteracies pedagogy and highlights the importance of strengthening teacher education programs and providing continuous professional development for teachers. © 30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings.

Victims of Educator-Targeted Bullying: A Qualitative Study on Teacher Harmful Experience

Bullying in school has become an international concern in recent years, and the issue became urgent after school closure during COVID Pandemic. International studies have identified teacher-targeted bullying by students as a real and harmful issue for teacher wellbeing. Our paper sets out discursive issues surrounding bullying against teachers as targets of intentional bullying. It reports on the findings of a small-scale, extant, qualitative research study on commenters' understanding of the antecedents of teacher-targeted bullying. The aim was to gain insights into the teachers´ targeted bullying from the perspective of teacher victims. We conducted a qualitative descriptive research design stemming from semi-structured interviews with victims of teacher-targeted bullying. A thematic content analysis of the data was generated from interviews with seventeen victimized teachers as a snowball sampling. The sample consisted of male (n = 7) and female (n = 10) participants from urban school locations in the capital of Czech Republic. The focus of our study was to determine how the teachers who had been experiencing bullying by their students described and perceived the nature and consequences attributed to such bullying. The findings indicate that the victims of teacher-targeted bullying were exposed repeatedly over long time verbal and nonverbal bullying, ignoring the teaching activities and other threats directed against teachers. Our results suggest bullying had a negative influence on the victims' private lives (family, colleagues), physical and mental health and self-esteem. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Identifying Variability in U.S. COVID-19 Response Through Temporal Partial Ordering Detection

We gain insight to the COVID-19 pandemic response by the various U.S. states through analysis of open source emergency declaration, mitigation, and response policy data. We propose ASNM + POD, a Partial Ordering Detection extension to the Adaptive Sorted Neighborhood Method to identify redundancies and implied temporal ordering requirements to understand how various U.S. states respond to COVID-19. We further strengthen the well-established ASNM entity matching method and address key limitations of its Longest Common Subsequence extension (ASNM + LCS) through detection of all temporal order requirements. Partial order requirements are determined probabilistically through empirical review of all records' time-ordered event sequences. We demonstrate effectiveness against a COVID-19 U.S. state policy dataset comprised of daily time-series data pulled from February and October 2022, where attributes are partially and variably populated. ASNM + POD yielded an F1 of 0.995 and an MCC of 0.985, significantly outperforming both ASNM and ASNM + LCS with F1/MCC improvements of 22%/50% and 15%/37%, respectively. Finally, we highlight the limited consensus on policies enacted, the variability in timelines of policy activations/deactivations, and activity at and after the two-year mark. © 2022 IEEE.

Systematic Literature Review of Students' perception of Employability Skills

CONTEXT Over the years, research investigating how engineering education contributes to the employability skills of students has led to the adoption of scenario-, problem- or project-based learning being implemented as effective methods for developing skills. Measuring student perception has emerged as an effective tool to gain insights into how changes to engineering curricula can contribute to various skills and attributes of engineering graduates. The COVID-19 pandemic has, however, disrupted teaching methods, making student engagement challenging. The effectiveness of teaching methods is dependent on the students' engagement level, which in turn translates into developing their employability skills. PURPOSE OR GOAL In order to pave the way for the post-pandemic approach towards improving the employability skills of engineers, it is important to gain a comprehensive understanding of the existing literature in this area of study. Thus, the aim of this study is to conduct a systematic literature review of undergraduate engineering students' perceptions of employability skills. APPROACH OR METHODOLOGY/METHODS Utilising the PRISMA protocol, a systematic review of the existing literature will be performed, looking at student perception of employability skills. The review will look at peer-reviewed research reporting on post-secondary engineering education in the last 20 years. Highly relevant papers will be chosen based on the protocol and reviewed. ACTUAL OUTCOMES Throughout the literature on this topic, a recurring theme is that employability skills are not well-defined, and a range of reference frameworks are used, such as accreditation requirements, 21st century skills and global engineer skills. The review found that the employers perceive that graduating engineers' non-technical skills are inadequate. In response, universities are constantly evolving their curricula and teaching methods to address this gap. Mismatches are identified in terms of the student perceptions of important employability skills and the perceptions of universities and industry employers. Internships, job placements, and problem- and project-based learning have found their place in helping undergraduate students to develop their skills. Suggestions for future work include a comparison with other professional degrees and how engineering education has deviated from these other degrees. CONCLUSIONS/RECOMMENDATIONS/SUMMARY The effect of COVID-19 on engineering student's employability and how long it will persist is currently unknown. This study contributes to the understanding of student perceptions about employability skills before the pandemic to understand the state of play when the COVID-19 disruption to teaching and learning occurred. It adds to the growing body of knowledge on engineering education focussed on employability skills and will help develop this field progress as we emerge from the pandemic. Copyright © Karthikaeyan Chinnakannu Murthy and Tania Machet, 2021.

Conducting Cross-Cultural Research on COVID-19 Memes

A cross-linguistic study of COVID-19 memes should allow scholars and professionals to gain insight into how people engage in socially and politically important issues and how culture has influenced societal responses to the global pandemic. This preliminary study employs framing analysis to examine and compare issues, actors and stances conveyed by both English and Chinese memes. The overall findings point to divergence in the way individuals communicate pandemic-related issues in English-speaking countries versus China, although a few similarities were also identified. 'Regulation' is the most common issue addressed by both English and Chinese memes, though the latter does so at a comparatively higher rate. The 'ordinary people' image within these memes accounts for the largest percentage in both data sets. Although both Chinese and English memes primarily express negative emotions, the former often occurs on an interpersonal level, whereas the latter aims at criticizing society and certain group of people in general. Lastly, this study proposes explanations for these findings in terms of culture and political environment. © 2022 Association for Computational Linguistics.

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.

Social Media Sentiment Analysis Related to COVID-19 Vaccines: Case Studies in English and Greek Language

SARS-CoV-2 and its mutations are spreading around the world, threatening the human population with millions of infections and deaths. Vaccines are considered the main available weapon at hand to mitigate the spread. As a result, the development of efficient systems to understand and supervise the information dissemination, as well as the evolution of sentiments towards vaccines is critical. The goal of this research was to build and apply a supervised learning approach to monitor the dynamics of public opinion on COVID-19 vaccines using Twitter data. 1,394,535 and 61,077 tweets about COVID-19 vaccines, respectively in English and Greek, were collected, classified based on sentiment polarity and analyzed over time to gain insights into sentiment trends. Our findings reveal that overall negative, neutral, and positive sentiments were at 36.5%, 39.9%, and 23.6% in the English language dataset, respectively, whereas overall negative and non-negative sentiments were at 60.1% and 39.9% in the Greek language dataset. Policymakers and health experts could take into consideration social media sentiment analysis alongside other ways of evaluating public sentiment. Social media users are actively seeking and sharing information about pandemic-related topics, allowing governments to use social media to develop effective crisis management strategies, better inform the public with accurate and reliable news, and alleviate disease-specific concerns. © 2022, IFIP International Federation for Information Processing.

Agent-based modeling in global pandemic propagation

Agent-based modeling has been widely used in the simulation of global pandemics, which provides useful policy implications and helps contain the pandemic's spread. Through agent-based modeling (ABM), people gain insight into the transmission of the pandemic and develop better policies to contain its spread. This article introduces the existing agent-based models used in the pandemic, such as smallpox, H1N1, and COVID-19, and the conclusions about pandemic forecasting that the scientists have reached through ABM. The introduction also shows the development and improvement of ABM as the computational power increases. It has been concluded from the existing research that implementing contact tracing and lockdown regulations could contribute to the achievement of digital herd immunity and contain the spread of the pandemic. Currently, scientists are dedicated to making a more scalable version of the agent-based model to analyze the transmission of the virus on a global scale. © 2022 IEEE.

A Review of CNN on Medical Imaging to Diagnose COVID-19 Infections*

In this paper, we study the Convolutional Neural Network (CNN) applications in medical image processing during the battle against Coronavirus Disease 2019 (COVID-19). Specifically, three CNN implementations are examined: CNN-LSTM, COVID-Net, and DeTraC. These three methods have been shown to offer promising implications for the future of CNN technology in the medical field. This survey explores how these technologies have improved upon their predecessors. Qualitative and quantitative analyses have strongly suggested that these methods perform significantly better than the commensurate technologies. After analyzing these CNN implementations, it is reasonable to conclude that this technology has a place in the future of the medical field, which can be used by professionals to gain insight into new diseases and to help in diagnosing infections using medical imaging. © 2021, EasyChair. All rights reserved.

Beyond a binary of (non)racist tweets: A four-dimensional categorical detection and analysis of racist and xenophobic opinions on Twitter in early Covid-19

Transcending the binary categorization of racist and xenophobic texts, this research takes cues from social science theories to develop a four-dimensional category for racism and xenophobia detection, namely stigmatization, offensiveness, blame, and exclusion. With the aid of deep learning techniques, this categorical detection enables insights into the nuances of emergent topics reflected in racist and xenophobic expression on Twitter. Moreover, a stage wise analysis is applied to capture the dynamic changes of the topics across the stages of early development of Covid-19 from a domestic epidemic to an international public health emergency, and later to a global pandemic. The main contributions of this research include, first the methodological advancement. By bridging the state-of-the-art computational methods with social science perspective, this research provides a meaningful approach for future research to gain insight into the underlying subtlety of racist and xenophobic discussion on digital platforms. Second, by enabling a more accurate comprehension and even prediction of public opinions and actions, this research paves the way for the enactment of effective intervention policies to combat racist crimes and social exclusion under Covid-19. © 2021 IEEE.

Abu Dhabi universities response to covid-19 via Facebook posts

This paper is introduced to explore higher education institutions' practices and contents in the Facebook platform in Abu Dhabi during the early period of Covid-19 to gain insights into how and why they are utilizing these platforms. It helps in identifying internal opportunities for improving the development of digital content. The paper extracted four academic parameters that shed the light on the content of messages created by public and private universities which were: Promoting online teaching, promoting students service and support, promoting positive vibes, and promoting Covid-19 issues. Promoting positive vibes represents the highest number of posts especially for private universities. © 2021 IEEE.

An Examination of Professor-Student Interactions, Stem Learning Challenges, and Student Adaptation Decisions During Covid-19 Pandemic

The COVID-19 pandemic transformed STEM learning environments across U.S. institutions. However, the impact of this pandemic on learning and decision-making in students are yet to be fully understood. It is important to gain insights into student experiences during COVID-19 pandemic so that student and institutional resiliency can be improved during future pandemics. This research is part of a larger nationwide inductive research project with the purpose of developing theories to explain the learning experiences and decisions of undergraduate STEM students during the COVID-19 pandemic. A mixed-methods approach with purposive sampling was utilized to enroll 63 undergraduate STEM students from six U.S institutions. Data was collected through recruitment surveys, academic transcripts, and interviews. One-hour ZOOM interviews, gave research participants the opportunity to narrate their salient STEM learning experiences during the spring 2020 semester. Data was analyzed using the NVivo qualitative analysis software and Microsoft Excel for coding, categorizing, memo-ing, constant comparative analysis, and theme development. Also, Microsoft Excel was used to analyze demographic data from recruitment surveys and GPA data from the academic transcripts. Results from the analysis of 30 coded interview transcripts revealed an emergent theme - Professor-Student Interactions Impact Learning and Adaptation Decisions. The three key categories of this theme are: Professor-Student Interactions and Learning Challenges;Adaptation Decisions;and STEM Performance. The seven categories of Professor-Student Interactions are coded as: Online Instructional Delivery Methods;Professor Caring Attitudes;Professor Leniency;Professor Availability;Student Workloads;Professor Technology Proficiency;and Professor Teaching Resources. Positive professor-student interactions improve student learning experiences. Negative professor-student interactions worsen student learning challenges and are coded as: Illusion of Time, Procrastination;Lack of Focus;Challenge of Asking Questions;Poor Understanding;Poor Quality Assignments;Poor Intermediate Grades;Stresses;and Lowered Motivation. While most research participants experienced high stresses, a few of them experienced low or no stresses. To minimize the impact of COVID-related learning challenges on their STEM learning and performance, research participants made effective adaptation decisions coded as: Refined Scheduling;Alternate Learning Resources;Professor Office Hours;Teaching Assistants;Peer Collaboration;Relaxation Strategies;and Pass/Fail Options. Compared to the fall 2019 GPAs, the improved spring 2020 GPAs of research participants may be partially attributed to professor leniency, pass/fail option, and cheating. Findings indicate that while STEM professors were adjusting to COVID-modified teaching and learning environments, many STEM students were developing a sense of self-discipline, self-teaching, and independence. They relied on both professor and non-professor generated resources to improve their own STEM learning and performance. Lessons learned and best practices for improved professor-student interactions and student adaptation decisions are discussed for potential replication in STEM communities for improved adaptability and resiliency during future pandemics. Future research will focus on quantifying the long-term effect of the COVID-19 pandemic on STEM performance. © American Society for Engineering Education, 2021

Assessing the Effectiveness of a Flex Model for a Sustainability Course in the COVID-19 Learning Environment

The COVID-19 global pandemic has suspended conventional operations in engineering education and forced changes that will inform our practice for years to come. The need for engineering educators to adapt course designs in short time frames amidst the compounding uncertainty of safety protocols, operational postures, and accreditation requirements is unprecedented and still evolving. As teachers update classroom technology, content, rubrics instructional schemes and cohort assignments there is much uncertainty about how this will affect our students. This paper attempts to evaluate the impact on students of transitioning to a Flex-Model during the global pandemic of COVID-19. Specifically, to gain insight on students' perception on the interaction within the new model, their learning experience and well-being within the Sustainability course. Using principles from HyFlex literature, our R1 university created a flexible instructional model. This Flex-Model is designed to accommodate in-person and remote instruction for professors and students alike. Instructors were encouraged to flexibly incorporate face-to-face class meetings with opportunities for remote students to participate using video conferencing technology (i.e. blended course delivery). Instructors were asked to leverage synchronous online activities, and asynchronous online content as appropriate to the size of their class, availability of suitable classroom space, content, and course structure (e.g., lecture-based, discussion, recitation, project-based, lab, studio) while considering the location of the students and access to on-campus resources. This research strives to evaluate the effectiveness of the Flex-Model through the lens of the student experience in a Sustainability course due to its interdisciplinary nature and that all 6 of our engineering departments were represented within the class population. The course is a topics course requiring weekly readings, discussions, assignments, and quizzes. The class roster consisted of 92 students (10 graduate students) with two of the co-authors serving as instructors. Data from student surveys conducted before, and during the Fall 2020 semester were analyzed. Survey questions included both qualitative and quantitative prompts. © American Society for Engineering Education, 2021

Mathematics is different: Student and tutor perspectives from Ireland and Australia on online support during COVID-19

From March 2020, the Mathematics Support Centre at University College Dublin, Ireland, and the Mathematics Education Support Hub at Western Sydney University, Australia, moved wholly online and have largely remained so to the point of writing (August 2021). The dramatic and swift changes brought on by COVID-19, in particular to fully online modes of teaching and learning including mathematics and statistics support (MSS), have presented students and tutors with a host of new opportunities for thinking and working. This study aims to gain insight both from students and tutors about their experience of wholly online learning and tutoring in the COVID-19 era. In this sense, it represents a 'perspectives' study, the idea being that before we examine specific aspects of this experience, it would be best to know what the issues are. Employing a qualitative analysis framework of 23 one-on-one interview transcripts with tutors and students from both institutions in Australia and Ireland, we identified five key themes as central to the shared experiences and perspectives of tutors and students. In this study, we discuss three of these themes in relation to the new normal with the intention of supporting MSS practitioners, researchers and students going forward. The themes describe the usage of online support, how mathematics is different and the future of online MSS. © 2021 The Author(s) 2021. Published by Oxford University Press on behalf of the Institute of Mathematics and its Applications.

Analyzing public perceptions toward COVID-19 Vaccination process using social media and machine learning

Since the declaration of COVID-19 as a global pandemic, the world is disrupting socially and economically. COVID-19 vaccines are still in the human trials and the governments should understand the society attitudes towards the vaccinations acceptance/hesitancy in order to deliver more accurate plans and health messages. Social media represents a catalog of our daily-life communications and activities. In this paper, we utilized the power of social media and machine learning to gain insights and understand the public attitudes towards COVID-19 vaccinations. The peak of online vaccination conversation on a social media happened with the authorization of Moderna and Pfizer vaccines for the emergency usage. The sentiment analysis of the clustered tweets relevant to the vaccination topic reveals that most of the public opinions was neutral and target the understanding of the vaccination process, confirming its efficiency and safety, and the countries plans to distribute and secure doses for their residents. The second top sentiment analysis group has negative attitudes according to spreading claims of the vaccines productions, side effects, and the previously reported vaccinations trails in different historical pandemic periods. The reported analysis raised the emergency need for interactive communications with communities from different cultural and educational level to increase their vaccination awareness and validate the vaccines' associated news. The decision makers' deliverable speech should simplify the scientific terms, target the community fears and release any public concerns regarding the vaccination process and its distribution plans. © 2021 Association for Computing Machinery.