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- Creators: Nakagawa, Kathryn
- Creators: Jimenez Arista, Laura
The study focused on participants the Gila River Indian Community, a tribal community in southwest Arizona with approximately 23,000 enrolled members, who completed a higher education degree and sought to return to serve as professionals and/or leaders at their tribal nation. Interviews were conducted off-reservation in the Phoenix metropolitan area within a 30-day window and held during the month of September
2015. Interviews were analyzed using three iterative levels of content analysis. Findings suggest there can be three methods of belonging within Gila River: belonging by cultural practices, belonging by legal definition, and belonging by both cultural and legal definition. However, the three methods of belonging do not automatically equate to being accepted by other tribal members.
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.
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%.