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%.

Psychedelics have sparked widespread interest as they offer a potential new form of therapeutic treatment. This thesis paper explores the current and upcoming psychedelics that are being researched for their use in a therapeutic setting. The main substances discussed are lysergic acid diethylamide (LSD), methylenedioxymethamphetamine (ecstasy/molly/MDMA), and ketamine (esketamine). This paper also discusses the mechanism of action for each drug and the underlying research that has been found to support the ethical use of these substances alongside talk therapy

College athletics are a multi-billion dollar industry featuring hard-working student-athletes competing at a high level for national championships across a variety of different sports. Across the college sports landscape, coaches and players are always seeking an edge they can gain in order to obtain a competitive advantage over their opponents. While this may sound nefarious, the vast amounts of data about these games and student-athletes can be used to glean insights about the sports themselves in order to help student-athletes be more successful. Data analytics can be used to make sense of the available data by creating models and using other tools available that can predict how student-athletes and their teams will do in the future based on the data gathered from how they have performed in the past. Colleges and universities across the country compete in a vast array of sports. As a result of these differences, the sports with the largest amounts of data available will be the more popular college sports, such as football, men’s and women’s basketball, baseball and softball. Arizona State University, as a member of the Pac-12 conference, has a storied athletic tradition and decades of history in all of these sports, providing a large amount of data that can be used to analyze student-athlete success in these sports and help predict future success. However, data is available from numerous other college athletic programs that could provide a much larger sample to help predict with greater accuracy why certain teams and student-athletes are more successful than others. The explosion of analytics across the sports world has resulted in a new focus on utilizing statistical techniques to improve all aspects of different sports. Sports science has influenced medical departments, and model-building has been used to determine optimal in-game strategy and predict the outcomes of future games based on team strength. It is this latter approach that has become the focus of this paper, with football being used as a subject due to its vast popularity and massive supply of easily accessible data.

College athletics are a multi-billion dollar industry featuring hard-working student-athletes competing at a high level for national championships across a variety of different sports. Across the college sports landscape, coaches and players are always seeking an edge they can gain in order to obtain a competitive advantage over their opponents. While this may sound nefarious, the vast amounts of data about these games and student-athletes can be used to glean insights about the sports themselves in order to help student-athletes be more successful. Data analytics can be used to make sense of the available data by creating models and using other tools available that can predict how student-athletes and their teams will do in the future based on the data gathered from how they have performed in the past. Colleges and universities across the country compete in a vast array of sports. As a result of these differences, the sports with the largest amounts of data available will be the more popular college sports, such as football, men’s and women’s basketball, baseball and softball. Arizona State University, as a member of the Pac-12 conference, has a storied athletic tradition and decades of history in all of these sports, providing a large amount of data that can be used to analyze student-athlete success in these sports and help predict future success. However, data is available from numerous other college athletic programs that could provide a much larger sample to help predict with greater accuracy why certain teams and student-athletes are more successful than others. The explosion of analytics across the sports world has resulted in a new focus on utilizing statistical techniques to improve all aspects of different sports. Sports science has influenced medical departments, and model-building has been used to determine optimal in-game strategy and predict the outcomes of future games based on team strength. It is this latter approach that has become the focus of this paper, with football being used as a subject due to its vast popularity and massive supply of easily accessible data.