ARTICLE | doi:10.20944/preprints202005.0370.v2
Subject: Social Sciences, Cognitive Science Keywords: Awareness; Readiness; Covid-19; Bangladesh; Knowledge; Attitude; Practice
Online: 25 June 2020 (15:57:11 CEST)
Bangladesh has adopted some special steps to control the quick spread of the COVID-19 pandemic situation. However, the local residents’ knowledge, attitudes, and practices towards the disease have a direct impact on the success of the controlling measures taken by the state. This article explores knowledge (K) about preventions, attitude (A) to the disease, and practices (P) of preventing COVID-19 situation of the young age groups residing in Bangladesh. Quantitative data were collected online using a KAP questionnaire from 932 participants. Results show the population is generally aware of the symptoms, keeping social distance by staying home and are concerned about re-spreading after the lock-down period. However, they are quite unsure about the possible medicines frequently talked about in the media and the necessity of avoiding animal protein. One of the major limitations is, these findings should not be generalized due to the low number of participants compared to the total population in Bangladesh.
ARTICLE | doi:10.20944/preprints202006.0292.v1
Subject: Computer Science And Mathematics, Probability And Statistics Keywords: temperatures; humidity; population density; environment; Covid-19; generalized linear model
Online: 24 June 2020 (09:44:59 CEST)
The main goal of this article is to demonstrate the impact of environmental data on the spreading of Covid-19. In this research, data has been collected from 70 cities/provinces that are affected by Covid-19. Here, environmental data refers to temperatures, humidity and population density in each of these cities/provinces. This data has been analyzed using statistical models such as Poisson, Quasi-Poisson and negative Binomial. It is found that a negative Binomial regression model is the best fit for our data. Our results reveal that average high temperature is the vital factor to slow down the spread of Covid-19. In addition, higher population density found to be an important factor for the quick spreading of Covid-19 where it is quite impossible to maintain the social distance and the virus can spread easily.
ARTICLE | doi:10.20944/preprints202209.0378.v1
Subject: Computer Science And Mathematics, Mathematics Keywords: Mathematics; Factors; Success; Failure; Students; Teachers;
Online: 26 September 2022 (05:35:23 CEST)
Background: Bangladeshi students from science, technology, engineering, and mathematics (STEM) often struggle with solving many mathematical problems in different pedagogic contexts. They mostly lack the considerable prior learning or strong basics required to cope with the teaching and learning materials used at the undergraduate levels, which leads many students to take readmissions every year. Objective: This research aims at investigating the factors affecting the success and deficit of university undergraduate mathematics students in Bangladesh. The mixed-method research incorporates quantitative and qualitative data analysis on the students' and teachers’ perspectives regarding the issues. The authors focus more on categorizing the reasons influencing effective mathematics pedagogies than on identifying new or unknown causes. Methodology: This study is outlined in three phases. The phases include i. Exploratory qualitative survey ii. Quantitative triangulation survey, iii. Explanatory semi-structured interviews. Findings: First, the qualitative survey exposes the important factors that highlight the student’s success and failure in mathematics. Next, the quantitative data confirm that there are some similarities and dissimilarities between students’ and teachers’ perceptions. Also, the coefficient correlation analysis shows male students lack consistency and passion for study resulting in poor performances. Conversely, female students emphasize the inability to connect mathematical theories to real-life usages, curriculum loads, and unavailable resources as the reasons for underperformance. Finally, the interview data demonstrate the students attribute their failure to inadequate practices, memorizing habits, poor teaching, low motivation, and external distractions. Also, students acknowledge the necessity of steady practice, clear understanding, regular study, and working strategies for successful mathematics education. Teachers emphasize students’ clear concepts, aptitude, motivation, and curiosity for successful learning. Conclusion: This conclusion proposes a fresh start with the local mathematics pedagogic practices by analyzing teacher-student feedback on the success and failure factors impacted by varied individual and contextual elements. The study offers inclusive feedback on the part of both stakeholders. However, an open discussion or interaction between students and teachers might be needed to enhance mutual trust and understanding between them.
ARTICLE | doi:10.20944/preprints202206.0115.v1
Subject: Computer Science And Mathematics, Information Systems Keywords: Explainable machine learning; COVID-19; Vaccination uptake; Shapley values; Feature importance.
Online: 8 June 2022 (05:30:18 CEST)
COVID-19 vaccine hesitancy is considered responsible for the lower rate of acceptance of vaccines in many parts of the world. However, sources of this hesitancy are rooted in many social, political, and economic factors. This paper strives to find the most important variables in predicting the COVID-19 vaccination uptake. We introduce an explainable machine learning (ML) framework to understand the COVID-19 vaccination uptake around the world. To predict vaccination uptake, we have trained a random forest (RF) regression model using a number of sociodemographic and socioeconomic data. The traditional decision tree (DT) regression model is also implemented as the baseline model. We found that the RF model performed better than the DT model since RF is more robust to handle nonlinearity and multi-collinearity. Also, we have presented feature importance based on impurity measure, permutation, and Shapley values to provide the most significant unbiased features. It is found that electrification coverage and Gross Domestic Product are the strongest predictors for higher vaccination uptake, whereas the Fragile state index (FI) contributed to lower vaccination uptake. These findings suggest addressing issues that are found responsible for lower vaccination uptake to combat any future public health crisis.