Matching Items (4)
- All Subjects: Anxiety
- All Subjects: bag-of-words
- All Subjects: Student Engagement
- Creators: Jimenez Arista, Laura
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
- Resource Type: Text
Social media has become a prominent part of people’s life worldwide. It allows for easy communication and connection between family, friends, and even complete strangers. It has provided for an increased global interconnectedness and allows people to form new relationships. Despite these positives, there are also negative effects of social media, including its danger to mental health. With increased social media use, it is possible to develop an addiction, similar to any substance addiction. People may also experience various mental health disorders, like depression and anxiety disorders. The purpose of this review was to identify a clear relationship between social media use and the development of anxiety disorders. Anxiety disorders is a general term, encompassing the different types, but the main types focused on in this review are generalized anxiety disorder and social anxiety. This review also incorporates information on age in order to clarify if certain age groups are more affected by social media use than others.
The field of veterinary medicine can be rewarding, but also very demanding. Research has shown that many practicing veterinarians struggle with mental illness, and the profession has one of the highest suicide rates in the United States. Research has also shown that many veterinary students struggle with mental illness. It is important to further research the mental health of veterinary students and how that can correlate with one's mental health as a practicing veterinarian. The purpose of this project is to summarize findings of the literature concerning the mental health of veterinary students and to present a new resource, the Wisdom Vet app, that can potentially support the well-being of veterinary students.
We attempted to apply a novel approach to stock market predictions. The Logistic Regression machine learning algorithm (Joseph Berkson) was applied to analyze news article headlines as represented by a bag-of-words (tri-gram and single-gram) representation in an attempt to predict the trends of stock prices based on the Dow Jones Industrial Average. The results showed that a tri-gram bag led to a 49% trend accuracy, a 1% increase when compared to the single-gram representation’s accuracy of 48%.
During the global COVID-19 pandemic in 2020, many universities shifted their focus to hosting classes and events online for their student population in order to keep them engaged. The present study investigated whether an association exists between student engagement (an individual’s engagement with class and campus) and resilience. A single-shot survey was administered to 200 participants currently enrolled as undergraduate students at Arizona State University. A multiple regression analysis and Pearson correlations were calculated. A moderate, significant correlation was found between student engagement (total score) and resilience. A significant correlation was found between cognitive engagement (student’s approach and understanding of his learning) and resilience and between valuing and resilience. Contrary to expectations, participation was not associated with resilience. Potential explanations for these results were explored and practical applications for the university were discussed.