This study estimates the effect of district wealth on Arizona Empowerment Scholarship Account program participation using data from the Arizona Department of Education. We find that students from poor districts are not more likely to participate as school performance decreases.Conversely, those from wealthy districts do increase participation as school performance decreases. We briefly try to explain the observed heterogeneity through survey results and commenting on the program design.

As we count down the years remaining before a global climate catastrophe, ever increases the importance of teaching environmental history and fostering environmental stewardship from a young age. In the age of globalization, nothing exists in a vacuum, yet our traditional education system often fails to reflect the abundant connections between content areas that are prevalent outside of schools. In fact, many of the flaws of the field of education have been exacerbated by the COVID-19 pandemic and a forced transition to online schooling, with many educators reverting to outdated practices in a desperate attempt to get students through the year. The aim of this project was to design a unit curriculum with these issues in mind. This month-long environmental history unit engages students through the use of hands-on activities and promotes interdisciplinary connections. The unit can be taught in a physical, online, or hybrid American history class, and will hopefully inspire and motivate students to become environmental stewards as they look toward their futures on this planet.

The incidence of childhood obesity has become increasingly prevalent in the United States in recent years. The development of obesity at any age, but especially in adolescence, can have lasting negative effects in the form of cardiometabolic disease, increased incurred healthcare costs, and potential negative effects on quality of life. In recent years, a rising trend of obesity, in both adults and adolescents, has been observed in lower income and ethnic groups. Increased adiposity can be influenced by modifiable factors -(physical activity, caloric intake, or sleep) or by non-modifiable factors (ethnicity, genetic predispositions, and socioeconomic status). The influence of these factors can be observed in individuals of all ages, including infants. A common indicator of the development of childhood obesity is rapid weight gain (RWG) within an infant’s first year of life. The composition of the gut microbiome can act as a predictor for RWG and the development of childhood obesity. Infants are exposed to an immense microbial load when they are born and their gut microbiome is continually diversified through their method of feeding and the subsequent introduction to solid foods. While currently understudied, it is understood that cultural and socioeconomic factors influence the development of the gut microbiome, which is further explored in this analysis. The DNA from 51 fecal samples from infants ranging from 3 weeks to 12 months in age was extracted and sequenced using next-generation sequencing, and the resulting sequences were analyzed using QIIME 2. Results from alpha-diversity and beta-diversity metrics showed significant differences in the gut microbiome of infants when comparing groups based on baby race/ethnicity, household income, and mom’s education. These findings suggest the importance of sociodemographic characteristics in shaping the gut microbiome and suggest the importance of future studies including diverse populations in gut microbiome work.

This paper encompasses a reflection of my experience engaging Algebra 1 students in a math classroom. 5 main strategies were focused on: incorporating games in the classroom, asking students to create (projects, word problems, etc), using technology in the classroom, fostering student collaboration, and allowing student choice. Each strategy was implemented three times in the classroom, student feedback collected, and the level of student engagement was assessed.

I have designed a college-level course to help college-aged students build and maintain healthy friendships. Every week, students will engage in collaborative activities and learn a variety of topics related to friendship, including the benefits of friendship, barriers to friendship, and friendship maintenance mechanisms. As part of their final project, students will demonstrate their knowledge of making and maintaining healthy friendships by completing a case study in which students will be expected to apply their learnings from class to a chosen friendship and observe how the friendship changes as a result. In order to establish the need for the course I made, I first conducted a literature review on friendship, loneliness, and factors that may contribute to young adults having difficulties making friends.

This thesis first examines the history and contemporary landscape of school mental health, offering evidence for schools as an essential component of the child and adolescent system of care. It then provides contemporary discussion around the importance of design in public administration, as well as analyzes the current design model of school-based mental health services, including key actors, normative assumptions, and underlying conceptual models to demonstrate the outdated presumptions that have led to a model that is not designed to adapt to the unique needs of students, especially after the COVID-19 pandemic. Building on contemporary theory of design in public administration, I argue that the largely fragmented, decentralized, bureaucratic, complex, and underdeveloped design of school-based mental health services mainly developed in the 1970s and 1980s has reached its limits and cannot adapt to new societal variables. Lastly, I discuss said limitations of this model to argue for a conceptual and practical re-design of the current system of school-based mental health systems in the United States.

Machine learning is a rapidly growing field, with no doubt in part due to its countless applications to other fields, including pedagogy and the creation of computer-aided tutoring systems. To extend the functionality of FACT, an automated teaching assistant, we want to predict, using metadata produced by student activity, whether a student is capable of fixing their own mistakes. Logs were collected from previous FACT trials with middle school math teachers and students. The data was converted to time series sequences for deep learning, and ordinary features were extracted for statistical machine learning. Ultimately, deep learning models attained an accuracy of 60%, while tree-based methods attained an accuracy of 65%, showing that some correlation, although small, exists between how a student fixes their mistakes and whether their correction is correct.

Using a dataset of ASU students from the 2016-2017 cohort, we interact gender and parent education level to observe gaps in academic achievement. We see a statistically insignificant achievement gap for males across parent education level, but a statistically significant achievement gap for females across parent education level. We also observe dropout gaps among these interaction groups. We see the widest dropout gap being between males across parent education level, with the smallest dropout gap being between females across parent education level. So with males we see an insignificant achievement gap but the widest dropout gap across parent education level, and with females we see a significant achievement gap but the smallest dropout gap across parent education level. What is driving these gaps and causing more similarly performing students to drop out at wider rates? At the aggregate level, we see larger gaps in grade- associated dropout probability across parent education level for males which may be able to explain the larger difference in overall proportions of dropouts between males. However, when predicting dropout probability of the semester with the most first generation and non-first generation dropouts, we see that females have the largest differences across parent education level in grade-associated dropout probability. This suggests that our model may be best suited in using college achievement data to predict overall dropout probabilities, not next-semester dropout probabilities using current semester data. Our findings also suggest that first generation students’ dropout probability is more sensitive to the grades they receive than non-first generation students.