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- All Subjects: Education
- Creators: School of Mathematical and Statistical Sciences
- Resource Type: Text
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health services, including key actors, normative assumptions, and underlying conceptual models to demonstrate the outdated presumptions that have led to a model that is not designed to adapt to the unique needs of students, especially after the COVID-19 pandemic.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.
Chandler Unified School District (CUSD), a large school system in Arizona that serves 45,000 students from preschool through high school, has been unable to escape similar structural and frictional inequities within its schools. One instance of a racially charged student performance at Santan Middle School motivated CUSD to take a more immediate look at equity in the district. It is during this response that our team of New Venture Group consultants engaged with Matt Strom, Assistant Superintendent of CUSD, in analyzing the important question of “how CUSD can take steps towards closing equity gaps within the district?”
CUSD defines an equity gap as any difference in student opportunity, achievement, discipline, attendance, etc. contributable to a student’s ethnicity, gender, or socioeconomic status. Currently, certain student populations in CUSD perform vastly different academically and receive different opportunities within schools, but as was our problem statement, CUSD is aiming to reduce (and eventually close) these gaps.
Our team approached this problem in three phases: (1) diagnosis, (2) solution creation, and (3) prevention. In phase one, we created a dashboard to help principals easily and visually identify gaps by toggling parameters on the dashboard. Phase two focused on the generation of recommendations for closing gaps. To achieve this goal, a knowledge of successful gap-closing strategies will be paired with the dashboard. In our final phase, the team of consultants created a principal scorecard to ensure equity remains a priority for principals.
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.