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Association between Student Engagement and Resilience in the Context of COVID-19

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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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Using Logistic Regression to Predict Stock Trends Based on Bag-of-Words Representations of News Article Headlines

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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
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Students’ Perceptions of Engagement in Online Courses and Its Effect on Academic Performance and Retention Rates

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Online learning in higher education has been increasing over the last two decades (NCES, 2016). Previous research has highlighted the importance of student engagement for academic achievement and performance (Fuller, Wilson, & Tobin, 2011; Northey et al., 2018). The current study aims to further understand students’ perceptions of…

Online learning in higher education has been increasing over the last two decades (NCES, 2016). Previous research has highlighted the importance of student engagement for academic achievement and performance (Fuller, Wilson, & Tobin, 2011; Northey et al., 2018). The current study aims to further understand students’ perceptions of peer interactions, assess the application of the Theory of Involvement in online learning environments, and identify factors of student engagement. Data were collected from 1,514 undergraduate students enrolled in online courses at Arizona State University (Mage = 25.96 years old; SD = 7.64; 1,259 female, 232 male, 12 non-binary, and 1 gender fluid). The results of this dissertation study indicate that the vast majority of students (94% of the sample) want opportunities for peer interaction in their online courses. Confirmatory Factor Analyses were conducted to validate three of the primary measures and these measurement models were used in subsequent analyses. Structural Equation Modeling (SEM) revealed that students who demonstrated high levels of Academic, Online Community, Life Application, and Social Engagement were more likely to perform well on measures of Academic Performance (i.e., doing well on quizzes or tests, earning higher letter grades). Additional SEM analyses indicated that sense of a community was related to all four aspects of student engagements. There was evidence that certain pedagogical factors were also associated with higher rates of student engagement. For example, students who reported high levels for Instructional Design (e.g., felt the course objectives were clear) were more likely to be academically engaged (i.e., demonstrated strong study habits). Lastly, while there were no significant differences in student engagement by gender, ethnicity, or living arrangements, students who valued peer interaction were more likely to report higher levels of Online Student Engagement. The findings of this research emphasize the desire online students have to interact with their peers, demonstrates the importance of engaging online students, and serves as a guide for educators in creating online courses that foster student engagement.
ContributorsCortes, Khaerannisa (Author) / Ladd, Becky (Thesis advisor) / Ladd, Gary (Committee member) / Thompson, Marilyn (Committee member) / Updegraff, Kimberly (Committee member) / Arizona State University (Publisher)
Created2021