Find My College is an app to help people who are interested in pursuing a collegiate degree; find a college/s that is right for them. This app is designed using the Ionic Framework, to allow access across all operating systems such as Android and MacOS. We wanted to create an app that people using Android or Apple can use, and this framework allows us to do that. The app is very user friendly and straightforward, which makes it usable to all types of people. It will be a free to use app that can be improved and adjusted if changes are needed/wanted.

As threats emerge, change, and grow, the life of a police officer continues to intensify. To help support police training curriculums and police cadets through this critical career juncture, this study proposes a state of the art approach to stress prediction and intervention through wearable devices and machine learning models. As an integral first step of a larger study, the goal of this research is to provide relevant information to machine learning models to formulate a correlation between stress and police officers’ physiological responses on and off on the job. Fitbit devices were leveraged for data collection and were complemented with a custom built Fitbit application, called StressManager, and study dashboard, termed StressWatch. This analysis uses data collected from 15 training cadets at the Phoenix Police Regional Training Academy over a 13 week span. Close collaboration with these participants was essential; the quality of data collection relied on consistent “syncing” and troubleshooting of the Fitbit devices. After the data were collected and cleaned, features related to steps, calories, movement, location, and heart rate were extracted from the Fitbit API and other supplemental resources and passed through to empirically chosen machine learning models. From the results of these models, we formulate that events of increased intensity combined with physiological spikes contribute to the overall stress perception of a police training cadet

The pandemic that hit in 2020 has boosted the growth of online learning that involves the booming of Massive Open Online Course (MOOC). To support this situation, it will be helpful to have tools that can help students in choosing between the different courses and can help instructors to understand what the students need. One of those tools is an online course ratings predictor. Using the predictor, online course instructors can learn the qualities that majority course takers deem as important, and thus they can adjust their lesson plans to fit those qualities. Meanwhile, students will be able to use it to help them in choosing the course to take by comparing the ratings. This research aims to find the best way to predict the rating of online courses using machine learning (ML). To create the ML model, different combinations of the length of the course, the number of materials it contains, the price of the course, the number of students taking the course, the course’s difficulty level, the usage of jargons or technical terms in the course description, the course’s instructors’ rating, the number of reviews the instructors got, and the number of classes the instructors have created on the same platform are used as the inputs. Meanwhile, the output of the model would be the average rating of a course. Data from 350 courses are used for this model, where 280 of them are used for training, 35 for testing, and the last 35 for validation. After trying out different machine learning models, wide neural networks model constantly gives the best training results while the medium tree model gives the best testing results. However, further research needs to be conducted as none of the results are not accurate, with 0.51 R-squared test result for the tree model.

This creative project is a short story in the Gothic genre followed by an explanation of certain literary elements and decisions. The Gothic genre often explores supernatural and uncomfortable topics that can challenge the reader’s perception and understanding of the world. Through this means of storytelling, authors are given the opportunity to connect the supernatural with complex and sensitive topics that may be difficult or even taboo to speak about in certain locations and time periods. In this thesis, I embrace the traditions of the Gothic-genre with a story that focuses on the issues prevalent today. The years 2020 and 2021 have been unprecedented times for humanity. Technology continues to grow at an alarming rate, suicide rates of young people have been on the rise for years, and a global pandemic has people adapting to all new ways of living. During these ever changing times, it is the Gothic that may provide guidance through these uncertainties by shedding light on the problems that will plague humanity both today and tomorrow. The story follows an outcast from society who aids in the creation of a divine monster, and the consequences that follow.

The purpose of this thesis is to create and evaluate an honors project for the CSE 325 Embedded Microprocessor Systems course at Arizona State University (ASU). It encourages students to expand upon the skills they learn in class and practice new skills that prove to be useful in industry. This is accomplished through implementing an Adafruit mini sound board using the UART communication protocol. The project’s success was measured with a survey taken by the participating students. The results indicated that the project was enriching and provided valuable experience. After further improvements, the goal is for this project to be offered each semester for students of Barrett, the Honors College in CSE 325 to complete as an honors contract.