Abstract
While student data systems are nothing new and most educators have been dealing with student data for many years, learning analytics has emerged as a new concept to capture educational big data. Learning analytics is about better understanding of the learning and teaching process and interpreting student data to improve their success and learning experiences. This paper provides an overview to learning analytics in higher education and more specifically, in e-learning. It also explores some of the issues around learning analytics.
Learning Analytics
As the amount of educational data grows, it is becoming challenging for higher education institutions to organize and understand large complex data sets. More than any time in the history, learners are leaving digital traces in their choices when learning (UNESCO, 2012) and these traces or analytics provide invaluable opportunity for the learning institutions to increase their efficiency and effectiveness. “Learning analytics places a greater emphasis on the qualitative data that originate from learning behavior” (Becker, 2013, p. 63) while analyzing quantitative metrics. In the last few years, the Horizon Report included learning analytics (LA) as one of the emergent areas of research since 2011 and in the most recent edition it provided the following description for it:
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