Predictive Modeling to Forecast Student Outcomes and Drive Effective Interventions in Online Community College Courses
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| Title: | Predictive Modeling to Forecast Student Outcomes and Drive Effective Interventions in Online Community College Courses |
|---|---|
| Language: | English |
| Authors: | Smith, Vernon C., Lange, Adam, Huston, Daniel R. |
| Source: | Journal of Asynchronous Learning Networks. Jun 2012 16(3):51-61. |
| Availability: | Sloan Consortium. P.O. Box 1238, Newburyport, MA 01950. e-mail: publisher@sloanconsortium.org; Web site: http://sloanconsortium.org/publications/jaln_main |
| Peer Reviewed: | Y |
| Page Count: | 11 |
| Publication Date: | 2012 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education Two Year Colleges |
| Descriptors: | Academic Achievement, At Risk Students, Prediction, Community Colleges, Online Courses, Two Year College Students, Predictive Measurement, Predictor Variables, Models, Case Studies, School Holding Power, College Freshmen, Accounting, Decision Making, Data, Data Analysis, Computer Software, Computer Managed Instruction, Educational Technology, Computer System Design, Databases, Decision Support Systems, Distance Education, Web Based Instruction, College Instruction, Integrated Learning Systems |
| Geographic Terms: | Arizona, Indiana |
| 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.) |
| Abstractor: | As Provided |
| Number of References: | 17 |
| Entry Date: | 2012 |
| Access URL: | https://sloanconsortium.org/jaln/v16n3/predictive-modeling-forecast-student-outcomes-and-drive-effective-interventions-online-co |
| Accession Number: | EJ982673 |
| Database: | ERIC |
| 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.) |
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| ISSN: | 1939-5256 |
