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ERIC Number: EJ982673
Record Type: Journal
Publication Date: 2012-Jun
Pages: 11
Abstractor: As Provided
Reference Count: 17
ISSN: ISSN-1939-5256
Predictive Modeling to Forecast Student Outcomes and Drive Effective Interventions in Online Community College Courses
Smith, Vernon C.; Lange, Adam; Huston, Daniel R.
Journal of Asynchronous Learning Networks, v16 n3 p51-61 Jun 2012
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.)
Sloan Consortium. P.O. Box 1238, Newburyport, MA 01950. e-mail:; Web site:
Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education; Two Year Colleges
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: Arizona; Indiana