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Predictive Modeling to Forecast Student Outcomes and Drive Effective Interventions in Online Community College Courses
ARTICLE

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Journal of Asynchronous Learning Networks Volume 16, Number 3, ISSN 1939-5256

Abstract

Community colleges continue to experience growth in online courses. This growth reflects the need to increase the numbers of students who complete certificates or degrees. Retaining online students, not to mention assuring their success, is a challenge that must be addressed through practical institutional responses. By leveraging existing student information, higher education institutions can build statistical models, or learning analytics, to forecast student outcomes. This is a case study from a community college utilizing learning analytics and the development of predictive models to identify at-risk students based on dozens of key variables. (Contains 4 tables and 3 figures.)

Citation

Smith, V.C., Lange, A. & Huston, D.R. (2012). Predictive Modeling to Forecast Student Outcomes and Drive Effective Interventions in Online Community College Courses. Journal of Asynchronous Learning Networks, 16(3), 51-61. Retrieved December 1, 2020 from .

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