Predictive Modeling to Forecast Student Outcomes and Drive Effective Interventions in Online Community College Courses
| Autor: | Adam Lange, Vernon C. Smith, Daniel R. Huston |
|---|---|
| Rok vydání: | 2012 |
| Předmět: |
Decision support system
Medical education Higher education Multimedia Computer Networks and Communications business.industry Computer science Distance education Learning analytics Educational technology Academic achievement Online community computer.software_genre Education ComputingMilieux_COMPUTERSANDEDUCATION business computer At-risk students |
| Zdroj: | Online Learning. 16 |
| ISSN: | 2472-5730 2472-5749 |
| DOI: | 10.24059/olj.v16i3.275 |
| Popis: | Community colleges continue to experience tremendous 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 the huge volumes of 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|