Impact of Teachers' Support Strategies on Students' Engagement of Online College English Class Experiences

During the covid-19 pandemic, students' online learning quality is imbued with teachers' support strategies while students' learning engagement is another great indicator underlies their learning experiences. Through a questionnaire survey of 500 freshmen who have had their college English class online in 2022 fall, an investigation using exploratory factor analysis, Pearson correlation analysis, stepwise regression analysis and parallel mediator model reveals the impact of teachers' support strategies (the six dimensions of challenge, authentic context, curiosity, autonomy, recognition and feedback) on the learners' online college English learning engagement (the four dimensions of cognitive engagement, behavioral engagement, emotional engagement, social engagement), thus particular concern is also given to the correlation with students' online learning experiences. It was found that even under diversified and comprehensive guiding strategies from teachers, university students' online college English learning engagement is at the medium level, among which the cognitive engagement should be devoted more. The experimental data also shows that teachers' support strategies have significant influence on learners' engagement, especially teachers' feedback and challenge setting will stimulate students to involve more in their study. In addition, both teachers' support strategies and students' learning engagement involves significant reflection of learning experiences accordingly. Based on this learning concept, related proposals see different degrees of prominence reflected in online instructional design, teachers' and students' feedback literacy, and technology-enabled innovative teaching practice are put forward, in order to effectively play the role of teacher scaffolding, learning experiences enrichment and students' engagement enhancement of online English learning. © 2023 IEEE.

Research on the Relationship between Teacher Expectations and College Students' Online English Learning Engagement

As a critical influencing factor of learning engagement, teacher expectation plays a vital role in ensuring the quality of online teaching under COVID-19. This paper investigates the relationship between teacher expectations (three dimensions of teacher support, teaching interaction, and academic feedback) on students' online English learning engagement (three dimensions of cognitive engagement, behavioral engagement, and emotional engagement) in e-learning through a questionnaire survey of 513 college students. Pearson correlation analysis and multiple regression analysis were applied as research methods. The results manifest that college students' online English learning engagement was above average, but emotional engagement needs improvement. In addition, teacher expectations of teaching interaction positively and significantly predict English e-learning engagement. Based on this, the article puts forward suggestions on the future of online teaching from the aspects of online teaching design, feedback quality of teachers and students, innovative teaching practice of technology empowerment to effectively play the role of teachers as scaffolding and improve the effectiveness of online English teaching. © 2023 IEEE.

U.S. Public Transportation Ridership Analysis and Prediction based on COVID-19

In this paper, a research was conducted to analyse and predict the impacts of COVID-19 on public transportation ridership in the U.S. and 5 most populous cities of the U.S. (New York City, Los Angeles, Chicago, Houston, Philadelphia). The paper aims to exploit the correlation between COVID-19 and public transportation ridership in the U.S. and make the reasonable prediction by machine learning models, including ARIMA and Prophet, to help the local governments improve the rationality of their policy implementation. After correlation analyses, high level of significant and negative correlations between monthly growth rate of COVID-19 infections and monthly growth rate of public transportation ridership are decidedly validated in the total U.S., and New York City, Los Angeles, Chicago, Philadelphia, except Houston. To analyse the errors of Houston, we consult the literature and made a discussion of Influencing factors. We find that the level of public transportation in quantity and utilization is terribly low in Houston. In addition, the factors, such as the lack of planning law and estimation of urban expressways, the high level of citizens' dependence on private cars and pride of owning cars play a considerable roll in the errors. And the impacts can be predicted to a certain extent through two forecasting models (ARIMA and Prophet), although the precision of our models is not enough to make a precise forecast due to the limitations of model tuning and model design. According to the comparison of the two models, ARIMA models' forecasting accuracy is between 6% and 10%, and Prophet's forecasting accuracy is between 8%-12%, depending on the city. Since the insufficient stationarity, periodicity, seasonality of time series, the Prophet models are hard be more refined. © 2023 SPIE.

Comparison of the Performances for CAPM and Fama-French models During COVID-19

Since the outbreak of COVID-19, it has caused a startling stun to both society and economy in numerous nations, where different industries suffered unequally. This paper reviews the various performance of the Capital Asset Pricing Model (CAPM), and the Fama-French three-factor model and the five-factor model in different regions and industries. To metric the performance, various statistics models and scaling are applied including Pearson correlation, linear regression, R2 scores, t-test, etc. Specifically, this paper demonstrates the different performances of the CAPM model on the US and Egyptian stock markets, whereas using generalized method of moments in a panel data analysis to evaluate the performance in the U.S. market and the paired sample t-test and Wilcoxon signed-rank to evaluate the performance in the Egyptian market. The Fama-French three-factor model and five-factor model are both based on the U.S. market and analyze the model's performance (measured by significant level) in the U.S. market in general and in individual sectors, respectively. Whereas, in terms of three-factors model, the OLS estimation and relapse expected excess return are used onto the variables and multiple linear regression method was used to study the significance of factors in three sub-industries. Regarding to five-factors model, a multivariate regression with covariates and OLS estimation are the method for evaluation. These results shed light for deeply understanding the model and recognizing the impact on the security market of the COVID-19. © 2023 SPIE.

Are Tourists Interested and Finally Decide to Visit Tourist Attraction After Seeing Virtual Tour?

Despite the significant loss, pandemic Covid-19 had been an enabler for digital transformation, specifically in Indonesia. In the tourism industry, the emergence of virtual tours brings fresh fashion for people to spend their leisure time. Following the calming down pandemic, the innovation of virtual tourism or virtual tours can promote specific tourist locations, which are expected to increase interest and decisions to visit. This research intends to discover virtual tours' power to attract visitors. The researcher used a quantitative method with simple linear regression analysis and distributed questionnaires to 104 people who had participated in virtual tours using meeting platform such as Zoom, Google Meet, or Webex. The results of this study indicate that the virtual tour influences the tourists' interest and decision to visit, at a moderate correlation level based on Pearson Correlation Test, with a percentage of 34.6% and 30%, respectively, based on R2 result. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Analysis and Forecast of Demand for Cold Chain Logistics of Agricultural Products in Shanghai in the Context of The Covid Pandemic

Forecasting the demand for cold chain logistics of agricultural products will help to achieve the balance between supply and demand of agricultural products and promote the healthy development of the cold chain logistics industry of agricultural products. This paper collects relevant data from 2015 to 2020 in Shanghai, and uses grey correlation analysis to conduct correlation analysis on the factors influencing the demand for cold chain logistics of fresh agricultural products. The traditional GM (1,1) model, the new information GM (1,1) model and the metabolism GM (1,1) model are used to forecast the demand for cold chain logistics of agricultural products in Shanghai in the next five years respectively. The grey correlation analysis shows that the employees of the tertiary industry and the total import and export of goods have the greatest impact on the market demand of cold chain logistics and the results of the three GM (1,1) models show that the sum of squared errors of using the new information GM (1,1) model is smaller. Finally, using the new information GM (1,1) model to forecast the demand for cold chain logistics of agricultural products in Shanghai from 2021 to 2025, and it is found that the overall demand for agricultural cold chain in Shanghai is on an upward trend. © 2022 IEEE.

Analysis of the Impact of COVID-19 on the Development of National ICT Industry

The raging of COVID-19 has caused a huge impact on all countries. This paper selects China, which has adopted a "strict strategy” in response to the epidemic, to observe the correlation between changes in COVID-19 data and ICT statistics, so as to analyze the impact of COVID-19 on the ICT industry. Due to availability of the data, this paper mainly analyzes the impact on telecommunication industry, mobile Internet, Internet business and software industry, which are more consumption-oriented in the ICT industry. In this paper, data from different fields at different time periods are collected and organized into four sets of graphs, and each graph is analyzed using pearson correlation data model and simple linear regression model. It can be concluded that the revenue of ICT industry in different fields was affected differently during the epidemic period. The specific impact needs to be discussed according to the different types of business in relation to the development of the epidemic. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Academic performance and psychological well-being of students in distance learning during the COVID-19 pandemic in Kosovo

COVID-19 has heavily influenced the mental health and academic performance of students all around the globe. The purpose of this study was to investigate the academic performance and psychological well-being of students in distance learning during the COVID-19 pandemic in Kosovo. The study was conducted through social media channels from October 2020 to December 2021. One thousand five hundred and eighty-eight (N=1588) undergraduate students from Public and Privat Higher Education Institutions in Kosovo were surveyed using a user-designed online questionnaire. Data were analyzed through the Social Science Package (SPSS), version 21.0. Correlation analysis and regression were used to explore the relationship between students' academic performance and psychological well-being during distance learning. From Pearson correlation analysis, it is seen that academic performance and psychological well-being have very high significant positive correlations (r= .836**;p =.000). Results highlight that academic performance is a strong predictor (69.9%) of students' psychological well-being. The current study's findings can provide policymakers and professionals in education with valuable information on academic performance and psychological well-being as a consequence of the COVID-19 pandemic. Copyright © 2022 The Authors.

Big Data Statistics of Logistics Warehousing Supply and Demand in Key Cities of Central and Western China Based on Baidu Index and Warehouse in Cloud

Based on the Baidu Index, taking "warehousing"and "warehouse"as the keywords, the Baidu search index of "warehousing"and "warehouse"nationwide is statistically analyzed. It is found that the Baidu search index with "warehousing"and "warehouse"as the keywords has significantly increased before and after the COVID-19 epidemic, which shows that the basic role of logistics warehousing in the national economic and social development is increasingly obvious, and the corresponding demand for logistics warehousing is growing. Based on the big data of Warehouse in Cloud, incomplete statistics of "warehousing demand"of "demand location"in China's provinces are similar to the analysis of differences in the source places (regions and provinces) of different search groups through the "population portrait"of Baidu Index. The "warehousing demand"and "warehousing supply"of the key cities in central and Western China are counted. Focusing on the key cities in central and Western China, the correlation analysis of warehousing rent and demand area is carried out. It is found that, on the one hand, the regional logistics warehousing demand is 3 years (the lease term is less than 1 year or 1-3 years), with intra-period volatility. On the other hand, regional centers (National Central Cities) have absolute advantages in the attraction of regional logistics and warehousing. Furthermore, in recent years, due to the impact of the COVID-19 epidemic and extreme meteorological and geological disasters, the adverse impact on the regional economic and social development will show that the demand for logistics and warehousing will be interrupted, reduced and lagged, and the growth will be restored in subsequent years. The average rent of key cities in Western China is 22.52 yuan/m2·month, the average vacancy rate is 11.65%, and there are 1359 warehouses in the park. The average rent of key cities in the central region is 23.5 yuan/m2·month, the average vacancy rate is 13.86%, and there are 1070 warehouses in the park. From the perspective of rent, Changsha shows the highest rent, while Taiyuan shows the lowest rent. Furthermore, the vacancy rate of Chongqing and Xi'an are the highest and lowest, respectively. There is a correlation between the variable of warehousing rent in 2022 and the total retail sales of consumer goods in 2021 (Spearman correlation coefficient is significant). There is a correlation between the variable of average warehousing demand area in 2019-2021 and the sample of the third industry production value in 2021 and the sample variable of total import and export volume of goods in 2021 (Pearson correlation coefficient is significant). The variable of average warehousing demand area in 2019-2021 and the sample variable of resident population. There is a correlation between the total retail sales of social consumer goods in 2021 (Spearman correlation coefficient is significant). On the one hand, the statistical analysis of big data on the digital warehousing information platform can provide reference for the prediction of supply and demand of logistics warehousing and modern logistics service industry in the high-quality development of the region. On the other hand, the spatial econometric analysis of logistics industry and regional economic growth represented by logistics warehousing needs further research. CCS CONCEPTS •Human-centered computing ∼Collaborative and social computing ∼Collaborative and social computing theory, concepts and paradigms ∼Computer supported cooperative work © 2022 ACM.

Analysis of the Correlation between the Mental Stress of College Students and Their Learning Initiative during the Outbreak of COVID-19

The spread of COVID-19 has brought negative impacts on the life and study of college students and thus aggravated their mental stress in this environment. Existing research has focused more on the status quo of the problem while having ignored its process variability, and there is no research on the correlation between the mental stress of college students and their learning initiative during the outbreak of COVID-19. To this end, this paper conducts an analysis of the correlation between the mental stress of college students and their learning initiative during the outbreak of COVID-19. In-depth analysis was carried out by the factor analysis method in SPSS, and an evaluation model was built for the mental stress of college students during the outbreak of COVID-19 based on four stressors, with the process of how to obtain the evaluation results given. The grey correlation analysis method was used to quantitatively analyze the correlation between the mental stress of college students and their learning initiative during the outbreak of COVID-19, that is, the factors affecting college students' learning initiative during the outbreak of COVID-19 were investigated through the grey correlation analysis. Finally, the relevant experimental results were given © 2022, International Journal of Emerging Technologies in Learning.All Rights Reserved.

Correlation and Causation in the Perspective of COVID-19: An Empirical Study on Canonical Correlation Analysis

COVID-19 has an immense effect on the Globe, crossing 53,86,95,729 affected in more than 220 nations, with 63,18,093 individuals deceased. Various countries released COVID-19 protocols to enclose its spread to control the pandemic. This research article illustrates the Effect of COVID-19 on aged people (age>50), diabetes individuals, and individuals with smoking habits concerning the cause of death. An attempt has been made to identify the predominant variables for the cause of death due to COVID-19. IBM SPSS statistical tool enabled by Canonical Correlation Analysis (CCA) is used for simulation. Data were gathered from the Kaggle, an open repository for 2020. Based on the results obtained, predictions regarding the Cause and Effect of COVID-19 are discussed. © 2022 IEEE.

Comparison of Supervised Learning Models for COVID-19 Confirmed Cases Prediction using Correlation Analysis

The COVID-19 virus, which first appeared in 2019, has a strong contagious power and is highly spread by people's mobility. In this study, correlation analysis is used in statistical preprocessing of dataset which further used to predict the COVID-19 confirmed cases for next day. Data is divided into two sets by organizing the data set by data preprocessing using correlation analysis. The first dataset is Google Mobility Data of COVID-19 infection with six variables. The second dataset is Google Mobility Data of COVID-19 infection with two variables: (1) Retail stores and leisure facilities (2) Grocery stores and pharmacies. The results of predicting the number of confirmed cases are compared using four supervised machine learning models. Furthermore, the soft voting method is used to show more improved results than the individual performances of each method. © 2022 IEEE.

Analysis of the spread of COVID-19 in Ukraine using Google Trends tools

The paper is devoted to the analysis of the spread of the COVID-19 pandemic in Ukraine based on finding the correlation between search terms in Google search engine and laboratory-confirmed cases. Statistics were obtained from open sources. The analysis was performed on matrices based on the Pearson correlation coefficient. To do this, we analyzed 25 typical search phrases, and after grouping them-7 remained. The data were reduced to the same discreteness. Correlation matrices were calculated for each wave of the pandemic and for altogether. As a result, the correlation between search phrases and laboratory-confirmed cases was observed only in the second and third waves of the pandemic. Moreover, in the first wave, the preconditions for its occurrence were found;in the second-Pearson's correlation coefficient was 0.74, and in the third wave, it decreased to 0.57. Other correlations that are specific to each pandemic wave are also analyzed. Additionally, it was proved that polynomials of the 6th degree most effectively restore lost data. © 2022 IEEE.

Student Acceptance Towards Online Learning Management System based on UTAUT2 Model

Recently, education has changed from physical learning to online and hybrid learning. Furthermore, the outbreak of COVID-19 makes them more significant. An online learning management system (LMS) is one of the most prevalent approaches to online and distance learning. The acceptance of the students towards the LMS is significant and it can give either bad or good responses to ensure the success of LMS. However, the Universiti Tun Hussein Onn Malaysia (UTHM) has not yet implemented any study to examine their LMS. The Unified Theory of Acceptance and Usage of Technology (UTAUT2) model is used in this study to investigate students' Behavioral Intention and Use Behavior when using the LMS in UTHM. This study also introduces a new construct in UTAUT2 named Online Learning Value. 376 respondents took part in this survey. Descriptive Statistics, Reliability Analysis, Pearson Correlation Coefficient, and Multiple Linear Regression analysis were all used to analyze survey data. The outcome of this research is Performance Expectancy (β=0.129, p=0.014), Hedonic Motivation (β=0.221, p=0.000), Online Learning Value (β=0.109, p=0.036) and Habit (β=0.513, p=0.000) has influence on students' intention to use LMS. Besides that, Facilitating Conditions (β=0.481, p=0.000) are the most important factors in students' use behavior toward the LMS followed by Habit (β=0.343, p=0.000) and Behavioral Intention (β=0.239, p=0.000). By utilizing the UTAUT2 model, the constructs of technology acceptance related to students' adoption of LMS have been identified and may become a reference to the stakeholders for future enhancement. © 2022,International Journal of Advanced Computer Science and Applications. All Rights Reserved.

Regression Study on Influencing Factors of COVID-19 Diagnosis Rate and Mortality: A Global Perspective

The novel coronavirus pneumonia (COVID-19) refers to the pulmonary infection caused by the novel coronavirus (2019-nCoV), which has become an urgent public health event of global concern at present. In order to help local governments to find out the factors that curb the spread of COVID-19, we explored the influence factors that cause COVID-19 infection and death in the fields of economy, society, life, and health in this paper. Through correlation analysis, we found that COVID-19 transmission and mortality are relatively strongly associated with human development index (HDI), Median Age, human life expectancy, proportion of smokers, and GDP per capita. Further regression analysis and machine learning regression algorithms also confirmed that HDI, proportion of smokers, GDP per capita, and Median Age have significant effects on COVID-19 transmission and mortality, with GBDT performing best with R² of 0.585 and 0.415 per million confirmed cases and deaths, respectively. This study aims to explore the impact of relevant factors on COVID-19 in the international community, inform the development of measures to reduce diagnosis and mortality rates in countries, and improve the capacity to respond to such public health emergencies. © 2022 ACM.

Reevaluating Synthesizing Sentiment Analysis on COVID-19 Fake News Detection using Spark Dataframe

Some research uses the random forest model and sentiment analysis to detect COVID-19 fake news. However, there is still a research opportunity to apply the method to Indonesian Tweets and reevaluate the feature's performance. Our research aims to reevaluate synthesizing the sentiment analysis feature on detecting COVID-19 fake news on Indonesian Tweets by using the Spark Dataframe. We divide the stages of machine learning development into several steps, including collecting data using Tweepy and then applying sentiment polarity scores using Apache Spark. We apply random forest to classify fake news using the Spark MLlib. Further, we use model evaluation calculation through the level of Accuracy, Recall, Precision, and F1. The results show that applying the sentiment polarity calculation to our Tweet dataset labels 148 Tweets with positive sentiments, 118 Tweets with negative sentiments, and 99 Tweets with neutral sentiments. The Pearson correlation coefficient (PCC) feature score of Sentiment equals 0.056 and ranks fifth in the top feature correlation scores list. According to the experimental findings, the random forest model produces Accuracy = 0.787 for both models with sentiment analysis and without sentiment analysis. Which indicates that sentiment analysis provides no significance in the prediction model. © 2022 IEEE.

Mental health of students during the Covid-19 pandemic: The effects of different parental practices

Mental health issues are a serious problem globally and have worsened since the Covid-19 pandemic. School students are experiencing high levels of stress due to the closure of schools. Students have to quickly adapt to online learning with minimal guidance during the early stage of the pandemic. Subsequently, students are allowed to go to school on a rotation basis. Therefore, a conducive home environment with support from parents plays an important role in helping students to cope with the uncertainties during the pandemic. We conducted a cross-sectional survey study where 761 high school students, aged between 13 to 18 years old were recruited in Malaysia. There was 468 female and 293 male students who participated in this study. Students' mental health was measured using the Strengths and Difficulties Questionnaire (SDQ) while parental practices were measured using the Alabama Parenting Questionnaire. Parental practices were measured separately for father and mother in terms of positive parenting, involvement, poor monitoring and corporal punishment. Pearson correlation analysis showed that all parental practices were correlated significantly with mental health issues among high school students. However, based on the multiple regression analysis, only paternal poor monitoring, maternal corporal punishment, maternal positive parenting and paternal corporal punishment significantly predicted students' mental health with paternal poor monitoring being the strongest predictor of students' mental health. This study supported the importance of utilizing good parental practices in order to reduce mental health issues among students. © 2022 ACM.

Evaluating the Relationship of Information Systems to Crisis Management in Libyan Higher Education Institutions

Crisis management is witnessing rapid changes due to advances in information technology, and the development of information systems in various fields, especially in the field of higher education during the spread of the Corona pandemic, has led to the need to study the relationship between information systems and crisis management. This study aims to identify the degree of relationship between the application of the six procedures and controls for information systems and the five stages of crisis management from the point of view of workers in the information and documentation centers at the University of Benghazi, University of Tripoli and the Libyan International Medical University, Libya. The descriptive-analytical method was applied to this study in addition to analyzing the results using a statistical application called the Statistical Package for Social Sciences (SPSS), which was used to calculate the frequencies for each procedure and create tables and charts. To analyze the data, we used Mean and Standard deviations, standard error, Cronbach's alpha coefficient, and Pearson's correlation coefficient as a statistical relationship. The study determined whether or not there is a correlation between the independent variables, i.e., the six procedures and controls on the information systems scale (data, physical requirements, software requirements, networks and communications, data and information security, human resources) and the dependent variable, i.e., the five stages on the crisis management scale (the stage of discovery of warning signals, the stage of Preparedness and prevention, damage containment stage, activity recovery stage, learning stage). © 2022 IEEE.

Exploring Anxiety of Québec University Community during COVID-19 Pandemic via Machine Learning

Many studies showed that COVID-19 global pandemic had a negative impact on the mental health of post-secondary students over the world. To date, very few studies have been conducted in a university setting, not only with students but also with employees. Moreover, almost all studies were based on classical statistical analysis. In this study, we investigated the level of anxiety felt by the Quebec university community (students and employees) during COVID-19 pandemic. Especially, we focused on the generalized anxiety disorder (GAD-7) score with the help of classical data exploration and predictive machine learning techniques. We observed that the best predictive model of the GAD-7 score was provided by the CatBoost algorithm) reaching a squared Pearson correlation coefficient of r2 = 0.5656. Moreover, we also explored variable importance and interaction effects between variables involved in the predictive model obtained using SHapley Additive exPlanations (SHAP). © 2022 ACM.

Exploring spatially heterogeneous associations between COVID-19 cases and the influencing factors in England

Coronavirus pandemic (COVID-19), caused by the SARS-CoV-2 virus, has spread expeditiously around the world since early 2020 and led to a tremendous number of deaths, severely impacting overall human well-being. The pandemic largely affected economic and social activities. The beneficial way to slow down or prevent the transmission is to be well informed about the disease and how the virus spreads. Therefore, analyzing factors that affect the COVID-19 transmission was of great importance in disease control and policy decisions. Socio-demographic factors show considerable impacts on the rate of COVID-19 infection, but the correlations would vary both temporally and spatially. Generally, the global correlation coefficients of all variables rocketed at the beginning of the COVID-19 outbreak and plateaued at a high level eventually. Then localized correlations were also calculated to map the spatial distribution of correlation coefficients. Results show that in the north of England, all socio-demographic factors are highly related to the COVID-19 cases with figures above 0.75, arising from the climatic, cultural and economic differences. As time flowed for both 55+ age structure and GDP, the southern part experienced sustainable increases in correlation values, which eventually rose above 0.5 at most locations. This finding confirmed our expectation that the higher GDP was, the more COVID-19 cases were, since high GDP always accompanies by more entertainment activities and more chances for face-To-face human contact. However, the interesting point was that around London, the GDP maintained uncorrelated and even negatively correlated with the cumulative cases as time went by. As for the number of pubs, the overall spatial distribution of correlation coefficients experienced unremarkable changes at three-Time points. The variable was significantly correlated with COVID-19 cases in the north. In contrast, in the south values kept below 0.5. Overall, this study provides an interesting view on investigating the relative factors of the COVID-19 pandemic. © 2022 IEEE.