application was tested for its usability and practicality by a small sample of students. Users provided suggestions on how to make the application more versatile and functional, and confirmed that the application made first aid easier and was something that they could see themselves using. While this application is not aimed to replace the current physical guide solution completely, the findings of this project show that SmartAid has potential to stand in as an improved, easy to use, and convenient alternative for first aid guidance.
HackerHero is an educational game designed to teach children, especially those from marginalized backgrounds, computation thinking skills needed for STEAM fields. It also teaches children about social injustice. This project was focused on creating an audio visualization for an AI character within the HackerHero game. The audio visualization consisted of a static silhouette of a face and a wave-like form to represent the mouth. Audio content analysis was performed on audio sampled from the character’s voice lines. Pitch and amplitude derived from the analysis was used to animate the character’s visual features such as it’s brightness, color, and mouth movement. The mouth’s movement and color was manipulated with the audio’s pitch. The lights of Wave were controlled by the amplitude of the audio. Design considerations were made to accommodate those with visual disabilities such as color blindness and epilepsy. Overall the final audio visualization satisfied the project sponsor and built upon existing audio visualization work. User feedback will be a necessity for improving the audio visualization in the future.
In order to train the model, data was collected from the NBA statistics website. The model was trained on games dating from the 2010 NBA season through the 2017 NBA season. Three separate models were built, predicting the winner, predicting the total points, and finally predicting the margin of victory for a team. These models learned on 80 percent of the data and validated on the other 20 percent. These models were trained for 40 epochs with a batch size of 15.
The model for predicting the winner achieved an accuracy of 65.61 percent, just slightly below the accuracy of other experts in the field of predicting the NBA. The model for predicting total points performed decently as well, it could beat Las Vegas’ prediction 50.04 percent of the time. The model for predicting margin of victory also did well, it beat Las Vegas 50.58 percent of the time.
To do this, a 4-week long pilot curriculum was created, implemented, and tested through an optional class at I Am Zambia, available to women who had already graduated from the year-long I Am Zambia Academy program. A total of 18 women ages 18-24 chose to enroll in the course. There were a total of 10 lessons, taught over 20 class period. These lessons covered four main computational thinking frameworks: introduction to computational thinking, algorithmic thinking, pseudocode, and debugging. Knowledge retention was tested through the use of a CS educational tool, QuizIt, created by the CSI Lab of School of Computing, Informatics and Decision Systems Engineering at Arizona State University. Furthermore, pre and post tests were given to assess the successfulness of the curriculum in teaching students the aforementioned concepts. 14 of the 18 students successfully completed the pre and post test.
Limitations of this study and suggestions for how to improve this curriculum in order to extend it into a year long course are also presented at the conclusion of this paper.