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Activity Number: 230 - Innovative STEAMS Methdology Over STEM
Type: Topic Contributed
Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
Sponsor: Quality and Productivity Section
Abstract #301700 Presentation
Title: STEAMS Applications on Gaming Science and Analytics
Author(s): Mason Chen* and Luke Liu
Companies: Mission San Jose High School, Stanford OHS and Stratford School
Keywords: STEAMS; Car Racing; Clustering; Regression; ROI; FMEA

This paper will apply “STEAMS” methodology on Gaming Science and Analytics. Kids are playing video games too long and parents do not want kids to play video games since most video games are not developing critical thinking. Hill Climb Car Racing game was chosen based on learning Vehicle Physics Science. Technology is on Transportation and Safety applications. Based on the engineering failure mode analysis and return of investment (ROI), author can develop a systematic car upgrading system through statistical modeling to optimize car performance much quickly. The AI clustering analysis grouped the similar field stages with common challenges and science which has helped upgrade car to support multiple stages. Simple linear regression was conducted to quantify the ROI return (car travelling distance) of investment (car upgrading cost). The regression model accuracy has been improved from original 66% (random playing mode) to 92% (systematic playing mode). The ROI slope has been improved from 147.2 to 512.4 meter/upgrade unit. The statistical Mixture DOE is applied to optimize the upgrading strategy, enhance ROI, and to understand the vehicle mechanics.

Authors who are presenting talks have a * after their name.

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