Today, I begin sharing my praxis around learning analytics. That praxis includes designing and engineering feedback loops with xAPI.
2020 sucked, but there were silver linings. Commuting to the office came to a halt, and so did about four hours worth of transition activities between home and office suddenly appear every day. Add to that meetings that people just stopped happening. Consulting Elsevier where I manage their Learning Analytics strategy, the planning and architecture in 2019 executed in 2020.
Customers were using a product and content based on things I actually had some hand in. I spent 2020 preparing what happens to take an MVP and deliver all the value envisioned for it.
I can write diatribes about learning under lockdown. This year, I’ll do some heavy lifting this year to support the role praxis has in creating feedback loops. Praxis accelerates professional practice by encouraging constructive discourse around the mechanics, theory and criticism of how professionals perform their practice. Megan and I encouraged such praxis around learning analytics with xAPI Quarterly and xAPI Camps
Since 2017, as I learned how Elsevier works from the inside, I figured out how to jettison some dependencies on legacy, siloed structures. In that same timespan, xAPI Profiles matured, as did xAPI.
I’ve shifted from the theoretical—specification creation—to the practical. With the work I’ve done for countless organizations in modernizing learning architecture, Elsevier is the first place I’ve worked where I’ve been able to see projects through to their fruition. Because of the lockdown, my life slowed down enough to reflect, to evaluate, to process and learn and, ya know, do things differently.
Thanks to standard tuition reimbursement for professional development at Elsevier, I finished 36 weeks of coursework for my Lean/Six Sigma Black Belt certification from Villanova in May 2020. Those extra four hours a day really came in handy for that effort, let. me. tell. you. I acquainted myself with the statistics practices I learned through my Math Education minor. Now, with actual business priorities associated with them, I had needed context to understand the real power behind this math I knew for years—the theory and the practice—to put that knowledge to use.
We’re talking about praxis.
It’s probably important to consider some inherent conceits in my practice. I tend to think of the work I do needing to hold up for 100 years and, still, be re-composable. Not necessarily because my work is so good… so much as digital works using xAPI just might be in use that long. Think of how enduring SCORM is as a technology. Look up the oldest eLearning you have in your LMS. Consider the 47 BILLION dollars spent internationally last year on learning technology upgrades, around the globe. NOW consider how much money will be spent every year on NEW learning technologies (that are increasingly going to be based in xAPI because…. reasons…). And think of how much harder it will be for the next evolution in technology to supplant all this new infrastructure.
I can’t deliver anything related to xAPI that isn’t built to endure. Learning tools should fundamentally behave flexibly, nimbly, as if it was designed and engineered for professionals like us to be so easy to keep working with. I know I’ve done quality work when the workflows that engineers and authors and administrators and learners deal with around learning technologies is more fluid, clears the obstacles to have a rich and rewarding learning experience. My passions and expectations around quality in practices of learning technology may not be entirely rational on their surface, and I can acknowledge that much 😉
Anyway, a result of such passions is that I was very happy to have had the opportunity to collaborate on some papers. Kirsty Kitto of UT-Sydney introduced me to John Whitmer of Schmidt Futures Foundation and through several online chats over 2020, I we wrote a position paper for the Society of Learning Analytics Research on Creating Data for Learning Analytics Ecosystems. Through those online chats, John particularly got me thinking a lot more clearly about the telemetry of learning data as a bit distinct from the analytics. Telemetry is about the engineering of the data — how it’s structured, where it goes, how do you get it out into a report.
Currently, practice is usually limited to what can be analyzed based only by collecting and counting things in data produced by learning content. Hint: that’s just how you collect metrics.
It’s the ways you start to operate and make decisions, even as simple as what data or decision is made and when, that start leading down the road to a learning analytics strategy. When that telemetry can be governed in ways that align in one place with the business/learning/analytics strategy (eg. xAPI Profiles), we can scale this work. With scale and impact in mind, after the election in November, John approached Megan and I with an opportunity to contribute a policy paper for the incoming Administration. Reflecting our thoughts around the potential to rapidly scale the production, collection and analysis of educational data based on the potential in the xAPI Profile Server, the Day One Project published our position on Improving Learning through Data Standards for Educational Technologies.
Wanna talk about praxis too?
xAPI Profiles standardization runs through IEEE, led by yours truly. We meet on the fourth Tuesday of every month, 3:30 Eastern. With a team that includes Will Hoyt of Yet Analytics as vice/co-chair, we’re going to have agenda and action items (homework) for participants with a wide range of technical insight/ability. If you’re new to xAPI Profiles but you know you’re going to need to put them to work, this is a good time to jump on-board as we’re going to really start the work in March 2021 with the idea that we’ll wrap standardization by March 2023. Already, there are multiple efforts that will run concurrently.
IEEE 9274.2.1 identifies the JSON-LD xAPI Profile standard. The manual one needs to implement a data strategy implied by a JSON-LD document must be understood by, well, people. Not just academics or engineers, but like… anyone… that would have to actually have to make decisions based on a standard, knowing it kinda has to work more like how USB or lightbulbs work as standards than, say… well, SCORM. As a result, there will be a 9274.2.2 activity starting very soon to figure out what all has to go in that documentation.
If you want to work with xAPI in a way that’s easy enough just by “reading the instructions,” Get involved. Contact me to get notified and reminded for these (will-be) monthly discussions. 🙂
Next post? Maybe it’s time to look at a little bit of question-storming that’s going to result in some learning analytics strategy.