Introduction to Analytics

Learning analytics is “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environment in which it occurs” (Siemens, et al., 2011).  Many of the techniques associated with learning analytics, such as predictive modeling, come from the field of business intelligence.  While educational data mining and business intelligence efforts by institutional researchers have been in use for many years, according to a recent report by the US Department of Education titled “Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics”, while educational data mining focuses on the development of tools to discover patterns in data, LA is focused more on the application of those tools to support decision making.  Some of those applications include improving retention and completion rates, identifying at-risk students, improving student self-monitoring skills, program assessment, assessing first-year experience programs, and aiding in student degree planning.  In each of these applications, users (students, faculty, advisors, etc.) are presented with intuitive dashboards that visualizes learner performance data, predictions of success, and recommendations for action.

 

For more information, contact Chris Millet at Education Technology Services.

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