Using Data Analytics to Foster the Instructional Quality of Online Education

Using Data Analytics to Foster the Instructional Quality of Online Education

B. Jean Mandernach (Grand Canyon University, USA)
DOI: 10.4018/978-1-5225-2548-6.ch003
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The growth of online learning has spurred interest in how administrators can (and should) utilize data to drive teaching evaluations, decision-making and program oversight. Within the realm of higher education administration, online learning programs offer a distinct advantage over their campus-based counterparts: tangible artifacts. The reality of online teaching and learning is that every interaction creates a digital footprint of the teaching-learning dynamic. While researchers have actively explored how the data from these digital footprints can be used to enhance student learning, less attention has been given to how administrators can utilize data analytics to foster the instructional quality of online education. Beyond learning analytics, teaching analytics provide valuable insights that allow administrators to efficiently evaluate the quality of online teaching, proactively support faculty, and make informed program oversight decisions to maximize the online learning experience.
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Teaching Analytics

Data analytics have quickly become an integral component of a comprehensive approach to improved decision-making across higher education (Desouza & Smith, 2016). The value of data analytics rests in its ability to help administrators make more informed and more efficient decisions (which, in turn, results in more effective use of limited resources). From predictive analytics to learning analytics to institutional analytics, data-driven decision-making has the potential to inform virtually all aspects of the institution. Considerable research has explored the power of learning analytics to foster student achievement and tailor learning experiences to the needs of individual students (Johnson, Adams, & Cummins, 2012), but less attention has focused on the application of teaching analytics to enhance the faculty experience or instructional quality.

Just as student activity in the online classroom leaves a digital footprint to inform learning analytics, instructional activity creates an equally impressive array of data on teaching behaviors. Learning management systems have integrated analytic capabilities that have the potential to measure a range of teaching activities (Tobin, Mandernach, & Taylor, 2015); for example:

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