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- All Subjects: Student Engagement
- Creators: Jimenez Arista, Laura
eeds, especially as a way to ideologically counter the rise of Nazism and fascism in pre-World War 2 Europe; in short, rather than evolving out of best practices in education, the concept of student engagement emerged out of an intersection of educational, psychological, and even medical prescriptions set against a specific political backdrop. This study also examines the ways that power dynamics shift and teacher-/student-subjects occupy new roles as engagement becomes a prominent force on the pedagogical landscape, addressing specifically the ways teachers and their assignments enact a disciplinary and pastoral function, all with the intent of molding students into interested, interesting, and democratic subjects. This study closes by considering some of the implications of this new understanding of engagement, and suggests potential directions for the term as well as abandoning the term altogether.
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%.