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- All Subjects: pedagogy
- Creators: Nakagawa, Kathryn
Over the course of a 6-month period participant observation was conducted at two high school spoken word workshops and four interviews were completed with both teaching artists and young adult spoken word poets. Using spatial and critical pedagogy frameworks, this study found that Poetic Shift serves as a platform for youth to engage in the performative process of narratively constructing and reconfiguring their identities. Poetic Shift’s ideological position that attributes value and validation to the voices and lived experiences of each youth is an explicit rejection of the dominant paradigm of knowing that relegates some voices to a culture of silence. The point at which the present study deviated from most other literature on spoken word is where it offers a critique of Poetic Shift as a site of critical literacy and of the unreflexive rhetoric of student empowerment. The problematic presuppositions within the call for youth voice and in the linear, overly simplistic curriculum of Poetic Shift tend to reinforce the dominant relations of power.
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%.