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Although U.S. rates of college enrollment among 18-24 year olds have reached historic highs, rates of degree completion have not kept pace. This is especially evident at community colleges, where a disproportionate number of students from groups who, historically, have had low college-completion rates enroll. One way community colleges are

Although U.S. rates of college enrollment among 18-24 year olds have reached historic highs, rates of degree completion have not kept pace. This is especially evident at community colleges, where a disproportionate number of students from groups who, historically, have had low college-completion rates enroll. One way community colleges are attempting to address low completion rates is by implementing institutional interventions intended to increase opportunities for student engagement at their colleges. Utilizing logistic and linear regression analyses, this study focused on community college students, examining the association between participation in institutional support activities and student outcomes, while controlling for specific student characteristics known to impact student success in college. The sample included 746 first-time, full-time, degree-seeking students at a single community college located in the U.S. Southwest. Additional analyses were conducted for the 440 first-time, full-time, degree-seeking students in this sample who placed into at least one developmental education course. Findings indicate that significant associations exist between different types of participation in institutional interventions and various student outcomes: Academic advising was found to be related to increased rates of Fall to Spring and Fall to Fall persistence and, for developmental education students, participation in a student success course was found to be related to an increase in the proportion of course credit hours earned. The results of this study provide evidence that student participation in institutional-level support may relate to increased rates of college persistence and credit hour completion; however, additional inquiry is warranted to inform specific policy and program decision-making at the college and to determine if these findings are generalizable to populations outside of this college setting.
ContributorsBeckert, Kimberly Marrone (Author) / De Los Santos Jr., Alfredo G (Thesis advisor) / Thompson, Marilyn S (Thesis advisor) / Berliner, David C. (Committee member) / Arizona State University (Publisher)
Created2011
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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

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.

ContributorsEmmanuelli, Michelle (Author) / Jimenez Arista, Laura (Thesis director) / Sever, Amy (Committee member) / College of Integrative Sciences and Arts (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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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

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%.

ContributorsBarolli, Adeiron (Author) / Jimenez Arista, Laura (Thesis director) / Wilson, Jeffrey (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05