Defining Risk and the Use of Risk Assessments by Learning Specialists within NCAA Academic Units
An Exploratory Study
DOI:
https://doi.org/10.15763/issn.2376-5267.2019.1.6.105-126Abstract
The National Collegiate Athletic Association NCAA (2009) defines the term “risk” as a “student-athlete’s likelihood of graduation”. The ability of athletic departments and athletic academic professionals to identify which of their student-athletes might be considered at risk is less straightforward. While many departments use their own tools to assess risk, there has been little research on the subject. This exploratory study sought to generate a collective understanding of how athletic academic units across the country define the term “at-risk” and assess which of their student-athletes are at-risk in order to begin creating a framework for use across the industry. A survey was completed by 43 member of the National Association of Academic and Student-Athlete Development Professionals (N4A) who serve in learning specialist roles, within athletic academic units. The results showed that academic units, across NCAA institutions, do not have one clear definition of risk, but rather the definition falls into four distinct categories. The study also found that there are three initial risk assessments used most frequently to determine student-athletes’ risk upon arrival at the institution, while the continued risk assessments fall into six distinct categories of assessments. As this is an exploratory study, the researchers acknowledge that we are only scratching the surface in regards to the breadth` and depth of assessment usage to determine risk of student-athletes at NCAA institutions. Therefore, the implications for future research are discussed.
Keywords: Risk, assessment, initial assessment, continued assessment
References
Dennis, J.M., Phinny, J.S., & Chuateco, L.I. (2005). The role of motivation, parental support and peer support in the academic success of ethnic minority first-generation college students. Journal of College Student Development, 6(3), 223-236. DOI: https://doi.org/10.1353/csd.2005.0023
Department of Justice. (2016, August 11). Government publishing office. Retrieved July 23, 2017, from https://www.gpo.gov/fdsys/pkg/FR-2016-08-11/pdf/2016-17417.pdf
Educational Testing Services. (2018). The TOEFL test. Retrieved from ETS: https://www.ets.org/toefl
Harry, B. & Klinger, J. (2006). Why are so many minority students in special education? Understanding race & disability in schools. New York, NY: Teachers College Press.
Horton, J. (2015). Identifying at-risk factors that affect college student success. International Journal of Process Education, 83-102.
Johnson, J. (2013). Assessing academic risk of student-athletes: Applicability of the NCAA graduation risk overview model to GPA. NACADA Journal, 76-89. DOI: https://doi.org/10.12930/NACADA-13-041
Meyer, S. K. (2005, October). NCAA academic reforms: Maintaining the balance between academics and athletics. In Phi Kappa Phi Forum (Vol. 85, No. 3,p. 15). National Forum: Phi Kappa Phi Journal.
National Collegiate Athletic Association. (n.d.). Playing rules goals and objectives. Retrieved from http://www.ncaa.org/playing-rules/playing-rules-goals-and-objectives
National Collegiate Athletic Association. (2009). Graduation risk overview. Retrieved July 18, 2017, from https://web1.ncaa.org/GRO/pages/pdf/gro_educate.pdf
National Collegiate Athletic Association. (2017). 2017-2018 NCAA division I manual. Retrieved from: http://www.ncaapublications.com/productdownloads/D118.pdf
Petr, T. A., & McArdle, J. J. (2012). Academic research and reform: A history of the empirical basis for NCAA academic policy. Journal of Intercollegiate Sport, 5, 27–40. DOI: https://doi.org/10.1123/jis.5.1.27
Robbins, S., Allen, J., Casillas, A., Akamigbo, A., Saltostall, M., Campbell, R.,Gore, P. (2009). Associations of Resources and service utilization, risk level, and college outcomes. Research in Higher Education, 50, 101-118. DOI: https://doi.org/10.1007/s11162-008-9106-7
Robbins, S., Allen, J., Casillas, A., Hamme Peterson, C., & Le, H. (2006). Unraveling the differential effects of motivational skills, social, and self-management measures from traditional predictors of college outcomes. Journal of Educational Psychology, 98(3), 598-616. DOI: https://doi.org/10.1037/0022-0663.98.3.598
Sanz, D. L., & Whitner, P. A. (1987). A study skills program for student-athletes. Paper presented at American Association for Counseling and Development Annual Convention (pp. 21-25). New Orleans, LA.
Sedlacek, W. E. (2004). Why we should use noncognitive variables with graduate and professional students. The Advisor: The Journal of the National Association of Advisors for the Health Professions. 24(2), 32-39.
Wolverton, B. (2016). The hottest hire in athletics? Learning specialists. The Chronicle of Higher Education, 62(25), A14, Retrieved from https://www.chronicle.com/article/The-Hottest-Hire-in-Athletics-/235513