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- Member of: Barrett, The Honors College Thesis/Creative Project Collection
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.
In the United States, the word "earthquake" is extensively used. This natural disaster has a year-round impact on numerous states across the country. Earthquakes are simply more than a natural calamity; they also have a negative psychological impact. Earthquake safety measures are essential for ensuring citizens' safety. This paper proposes, a technique for evaluating earthquake safety activities and instructing individuals in selecting appropriate precautions. Earthquake protection using Reach.love plus Amazon Alexa is special in that it uses cutting-edge virtual reality technology. The platform developed by Reach.love takes earthquake prevention to a new and innovative direction. The feeling of presence in a VR headset linked within Reach.love, allows the user to feel that an earthquake is occurring right now. Additionally, each location includes audio instructions that explain what to do in specific scenarios. The user can practice and mentally train to respond appropriately when a real earthquake happens, comparable to a 3D drill. Finally, the user will be able to utilize Amazon Alexa for help within the rooms in Reach.love to improve the experience of earthquake safety training. For example, if the user speaks to Alexa during the simulation and says, "Alexa, turn off the audio instructions," Alexa will do so, and the user will no longer hear them. Alexa would be the user's personal assistant during the training of earthquake protection.