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

Autor:	Vernon C. Smith, Adam Lange, Daniel R. Huston
Jazyk:	angličtina
Rok vydání:	2012
Předmět:	Online Learning Learning Analytics Predictive Modeling Community Colleges Risk Levels for Online Students Faculty Theory and practice of education LB5-3640
Zdroj:	Online Learning, Vol 16, Iss 3 (2012)
Druh dokumentu:	article
ISSN:	2472-5749 2472-5730
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:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/557c323cbe5b43edbaf8a8622478a8af Zobrazit plný text záznamu View record in DOAJ