Engaging Undergraduate Engineering and Aviation Students to Explore Project Based Learning with regard to Community Impact using Data Analytics in Higher Education

Abstract

The work in progress presents a study on undergraduate engineering students and project-based learning (PBL) to understand community impact that supports research-to-practice using data analytics in higher education. This study expands on data analysis and visualization applications in PBL using Watson Analytics. In applying the decision techniques, the classroom environment outcomes and community impact are key to engineering education goals. Our approach will provide a framework to explore predictive models and the outcome of PBL techniques in engineering education to draw a conclusion with regard to decision methods and factors (e.g., the engineer design process). In using the smart data analysis and visualization tools such as Watson Analytics, the specific areas analyzed could contribute and aid in the learning environment. From this assessment, the PBL decision techniques within the classroom environment and the overall relationship to the industry created new methodologies (techniques) to explore community impact using data analytics. These practices are assessed from detail statistical analysis and the dataset for predictive modeling of the user community. As such, this study serves as a baseline in developing models to improve PBL methods using data analysis in engineering and aviation education for the examination of community impact.