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- All Subjects: Student Engagement
- Creators: Jimenez Arista, Laura
This mixed methods action research dissertation explores the implementation of an online learning community and how it influences the engagement of students in distance learning environments. The primary research question guiding this inquiry is: How and to what extent does the implementation of an online learning community influence undergraduate student engagement in online courses? A sequential triangulation design was used to analyze data collected from surveys and responses collected from study participants during a synchronous online focus group. The analysis of the results of the study provide interesting insight into the online engagement of students. Key findings from the study are: 1) the inclusion of diverse perspectives is important for students and they value having opportunities to share their knowledge with peers; 2) an online learning community is beneficial for student engagement and this type of model is one they would participate in the future; 3) students experience a disconnect with peers when engagement opportunities in online discussion platforms feel insincere.
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%.