Learning Analytics courses and degree programs both on- and offline have begun to proliferate over the last three years. As a result of this growth in interest from students, university administrators, researchers and instructors a workshop was convened at the Learning Analytics and Knowledge Conference 2017 in Vancouver, Canada. The broad aim of the workshop was to review how these educational efforts are impacting the field, how synergy between instructors might be developed to greater serve the field and what kinds of best practices could be developed. In the following summary we discuss themes that emerged as a result of this effort.
Learning Analytics courses and degree programs both on- and offline have begun to
proliferate over the last three years. Tens of thousands of students registered for online
courses such as DALMOOC [
The goals of Learning Analytics programs and courses are many and varied. From providing an introduction to the field for the general public to training people for particular roles within the burgeoning employment market for Learning Analysts (or whatever these individuals come to be called). The objective of this section of the workshop was to consider and discuss the underlying purpose of learning analytics programming at both the program and course level. Ryan Baker lead a discussion of the challenges in setting up learning analytics programs within higher education institutions and provided context around how course offerings influence the field overall, and how to balance the desires of different stakeholders such as employers, university administrators and researchers in defining the expertise that graduates should hold. Justin Dellinger gave a similar presentation about setting up micro-masters credentials within the MOOC format.
The second objective of the workshop was to discuss and develop ideas around content and the sequence in which that content should be taught. Discussions were held around determining the level of consensus that exists within the community about what content should be included within Learning Analytics programming and what the priorities across different cultural, geographic, research paradigms and educational settings might be. Leah MacFadyen lead a discussion on how this dialogue has evolved over the last five years and where it may go in the future.
The third aim of the workshop was to demonstrate useful tools and resources for teaching Learning Analytics. John Stamper gave an overview of advances in data sharing systems for teaching purposes including the development of LearnSphere.
Possibly the most challenging part of teaching Learning Analytics is ``walking the talk``, or in other words, thinking through how to incorporate Learning Analytics into the courses we teach and programs we run. In many cases, the courses taught by researchers become the first attempts at observing learning analytics in the wild but whether this ad hoc approach can be systematized remains to be seen. Although participants agreed this was a desirable aim there was little being done to implement it in the classroom. Both Vitomir Kovanovic and Xu Wang offered preliminary ideas about how this might occur.
We hope to push forward the conversation around strategies to codify teaching practices. To develop an understanding amongst practitioners as to what works and what does not work with respect to teaching Learning Analytics. To that end Paul Prinsloo and Sharon Slade provided a commentary on whether or not a code of ethics should be taught as part of learning analytics programming. We hope that this will develop a lasting conversation that can propel the field forward in the most effective, impactful and rigorous way.
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