Learning Analytics: What Is It?

Written by: Julie Smith

Note: This post is the first in an occasional series about learning analytics, based on the Handbook of Learning Analytics.

Concepts that are difficult to define are sometimes compared to trying to nail Jell-O to a wall. That analogy could certainly apply to learning analytics – there’s no shortage of definitions of ‘learning’ or ways to measure and analyze it. So the response to the question ‘What Is Learning Analytics’ (Lang et al., 2022) is a welcome framing of a complex topic; the authors present learning analytics through four different lenses:

First, learning analytics is a concern or a problem. That is, modern educational methods generate big data which needs to be analyzed. Not only does that require technical skills grounded in a sound approach, but it also raises issues related to privacy, ethics, and equity. The authors point out a “tension between learning as a creative and social endeavor and analytics as a reductionist process that is removed from human relationships” (p9).

Second, learning analytics is an opportunity. The data generated by learning management systems (such as Canvas and Blackboard) has created the possibility of gaining insight into learning – particularly the process – as opposed to just the product – of learning. 

Next, learning analytics has become a field of inquiry. What distinguishes it from other uses of data to improve education? The authors point to the idea of a ‘human in the loop’ as central to the field. That is, the goal is not to replace instructors or curriculum designers but rather to provide information to augment their decision-making. As a field, learning analytics has grown exponentially since its inception a little over a decade ago.

And, finally, it is a community. Focused around the Society for Learning Analytics Research, academics, researchers, educators, practitioners, and industry representatives have formed a community of practice.

This framework for understanding learning analytics through four different lenses provides a balanced approach to the promise and peril of using big data in educational contexts. Future posts will explore various methods and applications of learning analytics.


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