In 2011, the 1st International Conference on Learning Analytics and Knowledge defined learning analytics as the “measurement, collection, analysis, and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs” (EDUCAUSE Review, 2012). Additionally, learning analytics can be used in various ways (SoLAR, accessed 2021) including:
- Predicting student academic success and identifying students who are at risk of failing or dropping out.
- Providing timely, personalized learning feedback to all students.
- Supporting the development of student self-reflection.
- Providing institutions with empirical evidence on the success of various pedagogical decisions.
Learning analytics can be:
- descriptive (provide insight into the past),
- diagnostic (provide insight into why something happens), or
- prescriptive (provide insight regarding possible outcomes) (SoLAR, accessed 2021).
One of the most powerful uses of learning analytics is its ability to help institutions make data-informed decisions and leverage the “tsunami” of data created by various software systems across the campus.
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