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- Creators: Jimenez Arista, Laura
In the study, the researcher infused three Engagement Workshops into the WPC 101 curriculum alongside pre-existing assignments to afford students learning opportunities for a richer, deeper exploration and reflection on their first-semester experience. Students participated in a pre- and post-intervention survey, contributed written narratives and reflections, and six students completed individual interviews.
Results of the study, particularly the qualitative results, indicated (a) quality of relationships, (b) ASU community, and (c) campus environment emerged as variables that served as the ‘roots of engagement’ for these first-semester students Thus, the current work extended previous research on engagement by identifying the initial developmental aspects of engagement among first-semester, university students. The discussion included detailed explanations of the results, limitations, implications for research and practice, lessons learned, and conclusions.
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%.