The mandate to measurably improve science and engineering learning effectiveness remains an ongoing challenge within Education. Despite numerous limitations and deficiencies, teacher-centered didactic instruction remains the most prevalent mode of instruction in K-12 Science, Technology, Engineering, and Mathematics (STEM) courses.  In these settings, teachers spend most of the class period lecturing within a predominantly passive classroom environment where students are rarely engaged and are often less motivated to learn.  For many students, such docile training environments frequently result in substandard learning outcomes. Particularly, for underrepresented minority (URM) students, passive learning environments are not effective at addressing performance gaps, attrition rates and social challenges.  Accordingly, educators have been exploring the development and application of instructional best practices for active learning - including advanced Modeling & Simulation (M&S) tools to cultivate outcomes within individual, organizational, and team-based settings. 

In this study, we evaluated the effectiveness of implementing Game-based Learning (GBL), physics-based modeling and high-fidelity driving simulation to convey critical vehicle design and motion dynamics principals to a high-school STEM cohort.  Through hands-on participation, the two key design parameters that have been evaluated are: 1) the percent front-to-rear weight distribution, and 2) the percent front-to-rear tire stiffness distribution.  The experiment served as a primary instructional component for the National Summer Transportation Institute (NSTI), sponsored by the Department of Transportation (DOT).  The Institute served as an informal learning effort – conducted outside of organizationally sponsored courseware - during the summer of 2023 at the University at Buffalo.  The experimental content was pre- and post-validated by two additional URM college-age cohorts consisting of students from CSTEP (Collegiate Science and Technology Entry Program) and WiSE (Women in Science and Engineering), respectively.  To supplement quantitative simulator data, self-report questionnaires and brief surveys were issued pre- and post-experiment to gauge overall improvement of conceptual understanding, and to better understand individual learning preferences.  

Keywords
DIGITAL-GAME-BASED-LEARNING;EDUCATION;GAME TECHNOLOGY;HUMAN PERFORMANCE;LEARNING ANALYTICS;LEARNING STANDARDS;M&S;MOTION;SERIOUS GAMES;SIMULATORS;STEM

Additional Keywords
Vehicle Dynamics, Transportation, Content Validation, Active Learning, Physics-based Modeling