Fiscal Year	2020
Division	Research & Sponsored Programs
Department	NPS Naval Research Program
Investigator(s)	Kamel, Magdi N.
Sponsor	NPS Naval Research Program (Navy)
Summary	A critical aspect of naval aviation training is to evaluate and predict student performance in order to determine whether a student will be successful in training, which aviation pipeline a student will be most successful, and identify students needing remediation earlier in order to provide resources for success. The goal of this research is to conduct analytics on naval aviation training data using statistical and machine learning techniques in order to: 1) determine the set of metrics predictive of student performance; 2) reveal trends and patterns which may indicate where and when remedial action is needed; and 3) develop a predictive model of performance based on the identified metrics. The methodology proposed for this research is based on the CRoss-Industry Standard Process for Data Mining (CRISP-DM). The CRISP-DM process model includes six phases that address the main issues in data analytics/mining. The six phases are undertaken in a cyclical and iterative manner and include: Business/Mission Understanding, Data Understanding, Data Preparation, Modeling, Evaluation and Deployment. The research deliverables include a technical report, PowerPoint presentation, and one or more statistical/machine learning predictive models.
Keywords	Aviation Training Big Data Data Analytics machine learning
Publications	Publications, theses (not shown) and data repositories will be added to the portal record when information is available in FAIRS and brought back to the portal
Data	Publications, theses (not shown) and data repositories will be added to the portal record when information is available in FAIRS and brought back to the portal