Education has been at the forefront of many issues in Arizona over the past several years with concerns over lack of funding sparking the Red for Ed movement. However, despite the push for educational change, there remain many barriers to education including a lack of visibility for how Arizona schools are performing at a legislative district level. While there are sources of information released at a school district level, many of these are limited and can become obscure to legislators when such school districts lie on the boundary between 2 different legislative districts. Moreover, much of this information is in the form of raw spreadsheets and is often fragmented between government websites and educational organizations. As such, a visualization dashboard that clearly identifies schools and their relative performance within each legislative district would be an extremely valuable tool to legislative bodies and the Arizona public. Although this dashboard and research are rough drafts of a larger concept, they would ideally increase transparency regarding public information about these districts and allow legislators to utilize the dashboard as a tool for greater understanding and more effective policymaking.

The COVID-19 pandemic has forced educators since 2020 to shift from all in-person learning to virtual learning through applications like Zoom. Students are now part of a collage of faces including their teachers’ who often may be dealing with technical glitches, foreign-looking interfaces, and unintentionally disruptive students. On the other side, students may struggle to find a stable working environment as they learn from home. Distance learning has been explored well before 2020, but its necessity, given the nature of a virus that preys on in-person interaction, has forced itself to the top of relevant conversation. . The issues with distance learning in primary education have roots in long standing issues with the education system as a whole. Without greater public awareness of the woes in our education system, the status quo of declining academic success, teacher salaries, and increasing classroom sizes will continue in the future. The problems with distance learning specifically represent a much more everlasting issue that is lack of accountability and action of lawmakers who are able to make these reforms.

Due to the elimination of the established instructional methods from the impact of COVID-19, the implementation of mass synchronous learning created a new strain of educational experiences for students that took a toll on social interaction. In the Spring 2022 semester, a survey was conducted of students that were previously or currently enrolled in the principal undergraduate biological sciences course, BIO 340: General Genetics, to assess both the prevalence of social interaction in the lives of the students and the potential ways this information could be molded to improve student’s educational and motivational experience. The results of this survey indicated that there was a considerable lack of social interaction and motivation among students that have taken or are taking BIO 340. Through a process of collecting qualitative data of students by 1-on-1 interviews, the majority of students requested that professors communicate with each other to learn more about ways they can incorporate social interaction as external technological applications and tools have been developed. Students brought up many external tools that professors in other biological sciences courses have been utilizing to engage student-to-student interaction and found these resources to increase their level of understanding and motivation. The driving interest behind this creative project is to understand the importance of peer-to-peer learning that may help guide professors that are new to synchronous teaching so that they may increase their level of understanding and comfortability of accessing resources that students themselves have shown to increase their educational experiences. The mixed-method design served as a means to understand what types of social interaction enhance students’ education and motivation.

Significant health inequalities exist between different castes and ethnic communities in India, and identifying the roots of these inequalities is of interest to public health research and policy. Research on caste-based health inequalities in India has historically focused on general, government-defined categories, such as “Scheduled Castes,” “Scheduled Tribes,” and “Other Backward Classes.” This method obscures the diversity of experiences, indicators of well-being, and health outcomes between castes, tribes, and other communities in the “scheduled” category. This study analyzes data on 699,686 women from 4,260 castes, tribes and communities in the 2015-2016 Demographic and Health Survey of India to: (1) examine the diversity within and overlap between general, government-defined community categories in both wealth, infant mortality, and education, and (2) analyze how infant mortality is related to community category membership and socioeconomic status (measured using highest level of education and household wealth). While there are significant differences between general, government-defined community categories (e.g., scheduled caste, backward class) in both wealth and infant mortality, the vast majority of variation between communities occurs within these categories. Moreover, when other socioeconomic factors like wealth and education are taken into account, the difference between general, government-defined categories reduces or disappears. These findings suggest that focusing on measures of education and wealth at the household level, rather than general caste categories, may more accurately target those individuals and households most at risk for poor health outcomes. Further research is needed to explain the mechanisms by which discrimination affects health in these populations, and to identify sources of resilience, which may inform more effective policies.

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.

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.