Approved for public release, distribution is unlimited / The authors examine the history of immigrant military service in the United States, explore the motivations of noncitizen enlistees, and analyze the military performance of noncitizens relative to that of citizen enlistees. Information sources include a comprehensive review of literature, focused interviews with a small sample of noncitizen enlistees, and cohort data files of enlisted personnel who entered the military from 1990 through 1998. The history of noncitizen service corresponds roughly to the nation's history of immigration and naturalization policy, with military service having offered immigrants economic benefits, as well as a path toward assimilation. Service by noncitizens has also provided the country a way to meet its military manpower needs. The results of statistical analyses suggest noncitizens have lower predicted rates of first-term attrition, and higher estimated rates of retention beyond the first term and promotion to E-4. The authors conclude that noncitizens provide a valuable source of manpower, and fulfill important roles as influencers for the next generation. Thus, it may be worthwhile to provide noncitizens more information about enlistment opportunities, and to implement unique reenlistment incentives including expedited green-card status for family members. Future research should examine specific ethnic categories of interest within the population of noncitizens. / First Lieutenant, Turkish Army / Lieutenant Commander, United States Navy

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Community colleges are the largest segment of higher education institutions in the United States providing access to historically underserved populations and growing numbers of first generation college students. Increasing college degree attainment is a national priority with new expectations of accountability. Despite decades of educational research, community colleges have startling low completion rates. Within the framework of Tinto's theory of retention, a predictive analytics model could provide community colleges the opportunity to drive custom intervention and support services to students. The purpose of this study was to explore the utility of Biglan's taxonomy for categorizing courses for potential use in a data analytics model to identify students at risk of failure to complete. The quantitative census study used archival data from 1,759 students. Log-linear analysis was used to test the key research question as to whether there is a predictive relationship between type of course failed, as cross-categorized by the dimensions in Biglan's taxonomy, in the first term and failure to complete a degree or certificate within 6 years. The analysis showed that a more parsimonious model, based on the interaction term for the life/nonlife and pure/applied Biglan categories, appeared related to completion, although no standardized residual was significant. A larger and more diverse sample may be necessary to determine the true effectiveness of Biglan's taxonomy as a classification schema in a predictive analytics model of degree completion. Based on these results, first term course failure appears to be a logical point for programmatic support that could lead to higher levels of associate degree completion opening doors of employment opportunity through education, thus supporting social change.