School Uniform Requirements: Effects On Student Academic Performance

The research question addressed in this capstone and subsequent case study were: Do school uniform requirements have an effect on overall student academic performance at one area charter school? In order to gain insight into the complex topic of uniforms, a literature review was conducted. Topics explored included worldwide perspectives on school uniforms, school uniforms in American public schools, the various reasons for and against uniform implementation, and the factors that comprise academic achievement. Upon completing a literature review, a case study was conducted at an area K-8 charter school. The author used a mainly qualitative approach to the case study, by interviewing key stakeholders at the charter school and surveying staff and students in various grade levels. All interview and survey questions were developed to better understand individuals’ opinions towards uniforms in general and in relation to academic success. Additional quantitative measures were taken to analyze data related to uniform infractions and official handbook guidelines on uniforms. Results of the case study revealed that the uniform debate is all encompassing, affecting all individuals within the school community, and entirely subjective in nature. Ultimately, the research question was inconclusive. However, as seen in academic literature and through the author’s own case study, proponents believe that uniforms create community, which, in turn, eliminates negative behaviors and allows for students to focus on academic performance

Predicting sea surface wave and wind parameters from satellite radar images using machine learning

Accurate predictions of wave and wind parameters over oceans are crucial for various marine operations. Although buoys provide accurate measurements, their deployment is limited, which necessitates the exploration of alternative data sources. Sentinel-1, a satellite mission capturing Synthetic Aperture Radar (SAR) images with high coverage, presents a promising opportunity. However, establishing the relationship between SAR images and wave/wind parameters is not straightforward. This project aims to develop a machine learning model that can effectively extract this relationship. To accomplish this, data from all available buoys measuring significant wave height and wind speed in the year 2021 were utilized. The corresponding SAR images were located, and 2 km×2 km sub-images were extracted around each buoy. From each sub-image, a set of features were extracted. These sub-images and features served as input to train machine learning models capable of predicting buoy measurements, supplemented with model data as necessary. The project presents two final deep learning models: one utilizing only the extracted features and another employing both the sub-images and features. These multi-class regression models simultaneously predict significant wave height and wind speed. The model using only features achieved a Root Mean Square Error (RMSE) of 0.553 m for significant wave height and 1.573 m/s for wind speed. The model incorporating both sub-images and features achieved an RMSE of 0.459 m for significant wave height and 1.658 m/s for wind speed. The code for the project can be found on https://github.com/SEE-GEO/sarssw