scholarly journals Smartphone GPS Locations of Students’ Movements to and from Campus

2021 ◽  
Vol 10 (8) ◽  
pp. 517
Author(s):  
Patricia K. Doyle-Baker ◽  
Andrew Ladle ◽  
Angela Rout ◽  
Paul Galpern
Keyword(s):  
Student Engagement ◽  
Environmental Factors ◽  
University Students ◽  
Regression Models ◽  
Bayesian Regression ◽  
Daily Movement ◽  
Campus Visit ◽  
Canadian City ◽  
Campus Activities ◽  
Ability And Willingness

For many university students, commuting to and from campus constitutes a large proportion of their daily movement, and therefore it may influence their ability and willingness to spend time on campus or to participate in campus activities. To assess student engagement on campus, we collected smartphone GPS location histories from volunteers (n = 280) attending university in a major Canadian city. We investigated how campus visit length and frequency were related to characteristics of the commute using Bayesian regression models. Slower commutes and commutes over longer distances were associated with more time spent but less frequent visits to campus. Our results demonstrate that exposure to campus life, and therefore the potential for student engagement, may relate not just to whether a student lives on or near campus, but also to urban environmental factors that interact to influence the commuting experience.

Factors Affecting on Entrepreneurial Intention of University Students (Case Study on Wollo University)

2021 ◽  
Author(s):  
Abdu Kamil
Keyword(s):  
Environmental Factors ◽  
University Students ◽  
Family Background ◽  
Secondary Data ◽  
Entrepreneurial Intention ◽  
Primary Data ◽  
Personal Factors ◽  
Factors Affecting ◽  
College Of Business

Abstract Background: Entrepreneurship is essential in creating, fulfilling and forming a healthy economy. This study is conducted to investigate Factor Affecting on Entrepreneurial Intention: The case study on Wollo University Students. Some studies have been done in this area but only a few were conducted in Ethiopia. This research aims to address the gap that exists due to the weakness of previous studies to verify the factors that affect entrepreneurial intention and provide more clarification on the topic. Methods: For the purpose of this study explanatory research design was employed. The researcher used stratified random sampling to classify all participants into seven colleges and one school of law. From each stratum proportionally by using purposive sampling to select 226 respondents with graduate students from college of business and economics for the desire of the study. Both primary and secondary data were collected. Primary data were collected through structured questionnaire from 210 students. Secondary data were collected from previous studies and used as reference. Results: The correlation and regression analysis has been applied to see the relationship and how independent variables influence entrepreneurial intention. From the analyses it is confirmed that demographic factors have statistically insignificant effect on entrepreneurial intention, while personal factors, environmental factors and family background have a statistically significant effect on entrepreneurial intention. Conclusions: Based on the findings it is concluded that demographic factor does not affect entrepreneurial intention while personal factors, environmental factors and family background affect entrepreneurial intention.

scholarly journals Socioeconomic Classes of University Students in Pakistan as Predictors of their Personality Traits: A Case Study

2021 ◽  
Vol 2 (2) ◽  
pp. 205-221
Author(s):  
Rafaqat Ali ◽  
Furrukh Bashir ◽  
Rashid Ahmad
Keyword(s):  
University Students ◽  
Personality Traits ◽  
Regression Models ◽  
Negative Impact ◽  
Positive Impact ◽  
Socioeconomic Class ◽  
Lower Socioeconomic ◽  
Negative Impacts ◽  
The Impact

The current study was heading for determining the impact of Pakistani university students’ socioeconomic classes on their personality traits. Demographic and personality questionnaires were filled by available university students online. The stepwise regression technique facilitated to generate regression models to define impacts of different socioeconomic classes on students’ different personality traits. Different regression models highlighted the significant negative impacts of the middle upper socioeconomic class on Agreeableness, Extraversion and Neuroticism personality traits. The lower socioeconomic class was found to have positive impact on only one personality sub-trait self-discipline. Whereas, the upper lower socioeconomic class caused positive impacts on students’ trust sub-trait, Conscientiousness trait and negative impact on excitement seeking sub-trait of personality. The importance of these impacts of socioeconomic classes on different personality traits and the possible implications are discussed with respect to university students’ academic performance and academic behaviour.

scholarly journals Environmental Factors Associated With Loa loa Microfilaria Prevalence and Intensity in Diverse Bioecological Zones of Cameroon

2021 ◽  
Vol 2 ◽  
Author(s):  
Xavier Badia-Rius ◽  
Hannah Betts ◽  
Samuel Wanji ◽  
David Molyneux ◽  
Mark J. Taylor ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Regression Models ◽  
Polynomial Regression ◽  
Canopy Cover ◽  
Nonparametric Tests ◽  
Tree Canopy ◽  
Mean Annual Temperature ◽  
Loa Loa ◽  
Factors Associated ◽  
Tree Canopy Cover

Loiasis (African Eye Worm) is a filarial infection caused by Loa loa and transmitted by Chrysops vectors, which are confined to the tropical rainforests of Central and West Africa. Loiasis is a major impediment to control and elimination programmes that use the drug ivermectin due to the risk of serious adverse events. There is an urgent need to better refine and map high-risk communities. This study aimed to quantify and predict environmental factors associated with loiasis across five bioecological zones in Cameroon. The L. loa microfilaria (mf) prevalence (%) and intensity (mf number/ml) data from 42 villages within an Equatorial Rainforest and Savannah region were examined in relation to climate, topographic and forest-related data derived from satellite remote sensing sources. Differences between zones and regions were examined using nonparametric tests, and the relationship between L. loa mf prevalence, mf intensity, and the environmental factors using polynomial regression models. Overall, the L. loa mf prevalence was 11.6%, L. loa intensity 927.4 mf/ml, mean annual temperature 23.7°C, annual precipitation 2143.2 mm, elevation 790 m, tree canopy cover 46.7%, and canopy height 19.3m. Significant differences between the Equatorial Rainforest and Savannah region were found. Within the Equatorial Rainforest region, no significant differences were found. However, within the Savannah region, significant differences between the three bioecological zones were found, and the regression models indicated that tree canopy cover and elevation were significant predictors, explaining 85.1% of the L. loa mf prevalence (adjusted R2 = 0.851; p<0.001) and tree cover alone was significant, explaining 58.1% of the mf intensity (adjusted R2 = 0.581; p<0.001). The study highlights that environmental analysis can help delineate risk at different geographical scales, which may be practical for developing larger scale operational plans for mapping and implementing safe effective interventions.

scholarly journals Evaluation of First-Year University Students’ Engagement to enhance Student Development

2021 ◽  
Vol 17 (2) ◽  
pp. 113
Author(s):  
Hooi Sin Soo ◽  
Yenwan Chong
Keyword(s):  
Student Engagement ◽  
University Students ◽  
Student Development ◽  
Academic Engagement ◽  
University Education ◽  
First Year ◽  
Development Programs ◽  
Online Engagement ◽  
New Students ◽  
First Year University

Abstract: The COVID-19 crisis has dramatically impacted university education as well as created new challenges for tertiary learning institutions. The pandemic has exacerbated graduate unemployment and increased student dropout rates. In response to these unprecedented challenges, universities are formulating more student development initiatives to support new students to transition into university and produce holistic graduates with essential soft skills. Student engagement evaluation can help inform and enhance the implementation of student development programs. In this study, seven domains of first year university students’ engagement were evaluated namely Academic Engagement (AE), Beyond-class Engagement (BE), Intellectual Engagement (IE), Online Engagement (OE), Peer Engagement (PE), Student-staff Engagement (SE) and Transition Engagement (TE). This study found that university freshmen’s Online Engagement (OE) was the strongest while their Academic Engagement (AE) was the weakest. This study also discovered that first year university students’ engagement were weakest with regard to reading of textbooks before attending class, asking questions in class and borrowing books from the university library. Future student development programs targeted at first year university students could be enhanced by increasing the use of ICT in teaching and learning as well as increasing efforts in assisting new students to transition from school to university learning environments by inculcating good reading habits and encouraging active class participation. Keywords: Academic engagement, First year undergraduates, Student development, Student engagement, Transition to university

scholarly journals Factors Affecting Student Achievement in Faculty of Economics "X" University

The Winners ◽  
2015 ◽  
Vol 16 (1) ◽  
pp. 25 ◽  
Author(s):  
Lily Suhaily ◽  
Yasintha Soelasih
Keyword(s):  
Student Achievement ◽  
High Education ◽  
University Students ◽  
Random Sampling ◽  
Standard Of Living ◽  
Factors Affecting ◽  
The Difference ◽  
Campus Activities

Education is an important point for many countries, including Indonesia. People with high education in a country could make the country wealthier. Parents are usually aware that it is important for their children to be educated. If the children are well educated, they will increase the standard of living of their parents and their selves. Based on this phenomenon, research evaluated factors that influence student achievement and also evaluated the difference of achievement between students who are active in campus activities and are not active. A number of 329 questionnaires were distributed to Faculty of Economics, “X” University students using random sampling. The result shows that the students themselves as factor that could determine their achievement. The evaluation also found that the achievement of student who is active in campus activities is different with the achievement of student who is not active in campus activities.

scholarly journals Exploring the Health-Promoting Potential of the “parkrun” Phenomenon: What Factors are Associated With Higher Levels of Participation?

2018 ◽  
Vol 33 (1) ◽  
pp. 13-23 ◽  
Author(s):  
Verity Cleland ◽  
Meredith Nash ◽  
Melanie J. Sharman ◽  
Suzi Claflin
Keyword(s):  
Physical Activity ◽  
Environmental Factors ◽  
Regression Models ◽  
Online Survey ◽  
Multivariate Analyses ◽  
Health Gain ◽  
Cross Sectional ◽  
Physically Active ◽  
Health Promoting ◽  
Intentions To Participate

Purpose: “ parkrun” is a free and increasingly popular weekly 5-km walk/run international community event, representing a novel setting for physical activity (PA) promotion. However, little is known about who participates or why. This study aimed to identify sociodemographic, health, behavioral, individual, social, and environmental factors associated with higher levels of participation. Design: Cross-sectional. Setting: Tasmania, Australia; June 2016. Participants: Three hundred seventy two adult parkrun participants. Measures: Online survey measuring sociodemographic, health, individual, social and environmental factors, parkrun participation, and PA. Analysis: Descriptive statistics, zero-truncated Poisson regression models. Results: Respondents (n = 371) were more commonly women (58%), aged 35 to 53 years (54%), and occasional or nonwalkers/runners (53%) at registration. A total of 44% had overweight/obesity. Half had non-adult children, most spoke English at home, and 7% reported PA-limiting illness/injury/disability. Average run/walk time was 30.2 ± 7.4 minutes. Compared to regular walkers/runners at registration, nonwalkers/runners were less commonly partnered, more commonly had overweight/obesity, less physically active, and had poorer self-rated health. Multivariate analyses revealed relative parkrun participation was inversely associated with education level and positively associated with interstate parkrun participation, perceived social benefits, self-efficacy for parkrun, and intentions to participate. Conclusion: parkrun attracts nonwalkers/runners and population groups hard to engage in physical activity. Individual- and social-level factors were associated with higher relative parkrun participation. parkrun’s scalability, accessibility, and wide appeal confers a research imperative to investigate its potential for public health gain.

scholarly journals A Vision-Based Approach for Sidewalk and Walkway Trip Hazards Assessment

2020 ◽  
Vol 17 (22) ◽  
pp. 8438
Author(s):  
Rachel Cohen ◽  
Geoff Fernie ◽  
Atena Roshan Fekr
Keyword(s):  
Environmental Factors ◽  
Edge Detection ◽  
Gaussian Process ◽  
Regression Models ◽  
Gaussian Process Regression ◽  
Region Growing ◽  
Cost Effective ◽  
Second Phase ◽  
Cost Effective Method ◽  
Regression Techniques

Tripping hazards on the sidewalk cause many falls annually, and the inspection and repair of these hazards cost cities millions of dollars. Currently, there is not an efficient and cost-effective method to monitor the sidewalk to identify any possible tripping hazards. In this paper, a new portable device is proposed using an Intel RealSense D415 RGB-D camera to monitor the sidewalks, detect the hazards, and extract relevant features of the hazards. This paper first analyzes the effects of environmental factors contributing to the device’s error and compares different regression techniques to calibrate the camera. The Gaussian Process Regression models yielded the most accurate predictions with less than 0.09 mm Mean Absolute Errors (MAEs). In the second phase, a novel segmentation algorithm is proposed that combines the edge detection and region-growing techniques to detect the true tripping hazards. Different examples are provided to visualize the output results of the proposed method.

scholarly journals Robust Bayesian Regression with Synthetic Posterior Distributions

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 661 ◽  
Author(s):  
Shintaro Hashimoto ◽  
Shonosuke Sugasawa
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Bayesian Variable Selection ◽  
Bayesian Regression ◽  
Simulation Studies ◽  
Posterior Distributions ◽  
Linear Regression Models ◽  
Computation Algorithm ◽  
Shrinkage Priors ◽  
Variable Selection And Estimation

Although linear regression models are fundamental tools in statistical science, the estimation results can be sensitive to outliers. While several robust methods have been proposed in frequentist frameworks, statistical inference is not necessarily straightforward. We here propose a Bayesian approach to robust inference on linear regression models using synthetic posterior distributions based on γ-divergence, which enables us to naturally assess the uncertainty of the estimation through the posterior distribution. We also consider the use of shrinkage priors for the regression coefficients to carry out robust Bayesian variable selection and estimation simultaneously. We develop an efficient posterior computation algorithm by adopting the Bayesian bootstrap within Gibbs sampling. The performance of the proposed method is illustrated through simulation studies and applications to famous datasets.

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