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Author Mandernach, B. Jean ♦ Palese-Sanderson, Kelly
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format HTM / HTML
Language English
Abstract The growth of online learning mandates that institutions evaluate instructional effectiveness to ensure students receive a high-quality educational experience. While a number of rubrics exist to benchmark best practices in online teaching, advances in learning management technology are expanding opportunities for utilizing data analytics to effectively and efficiently monitor instructional quality. At present, learning management systems can track logins, activity patterns and time-on-task, but this represents only a fraction the possibilities. Predictive modeling may soon allow for more integrated analytics that can quickly and easily inform evaluations of online teaching.
Description Affiliation: Grand Canyon University (Mandernach, B. Jean; Palese-Sanderson, Kelly)
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2011-09-01
Publisher Place New York
Journal eLearn (ELERN)
Volume Number 2015
Issue Number 1


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Source: ACM Digital Library