Let me suggest that one of the biggest gaps in our thinking is about doing. Too often, we think about ‘learning’ as the end goal, and it’s not. (I’ve gone as far as suggesting we rename L&D!) We need to rethink and ask about doing, not learning; we need to ask about people: what to do?

To start with, for organizational needs we don’t learn for intellectual self-gratification. (Though, too often, it seems that way: ‘awareness’ courses continue to perplex me. What possible organizational value will they achieve?) Instead, there should be identified gaps that are targeted because remedying them will improve outcomes.

There’s a whole process of analysis that starts with looking at gaps between ideal and real performance. Where are we lacking? Then, for any particular gap, we look for the root cause: is it a lack of skill, lack of knowledge, lack of resources, lack of motivation, … ? This up front work keeps us from using training to address a misalignment between incentives and desired behavior, for instance. Training isn’t going to keep people from doing things that are in their best interest! (And rightly so.)

Then, when we identify the root cause, we can target the appropriate intervention. Not all interventions may be within L&D’s purview, of course. We design courses. We could also be the ones who design performance support and facilitate informal learning (who better?). Of course, we shouldn’t be responsible for hiring or resourcing or compensation; at least not without a job description and skilling rethink. Our organizations deserve to invest in things that will move important needles.

This all is a shift to a focus on performance, on doing, not learning. While there are a variety of terms, this, to me, falls under the label ‘performance consulting’. It starts by asking “what should people be doing?” With the caveat that they aren’t doing now, or are doing wrongly. Then we ask “why?” Finally, we’re ready to design a solution. We’re focusing on outcomes. If it’s skills, it has to be in the head; and learning’s involved. If it’s knowledge, if it has to be in the head, learning can be involved, otherwise we should put it in the world. And so on.

My intent here is to suggest focusing on performance, not learning or knowing. That makes a better focus for investment, and is easier to recognize when it’s been remedied. So, what to do? Focus on performance first. Determine if a learning solution is your best choice, before you invest in it. Otherwise, you could be throwing money away. If you’ve got money to throw away, I can help ;), but I’d rather help you use it wisely.

The Learning Development Accelerator is running a mini-conference on performance consulting. It’s four half-days of immersion in the topics, with some of the top folks in the field. All with the usual focus on evidence-based practices. If you want to start doing L&D right, it’s a good start! 

In working with colleagues about redesigning design (our goal is better incorporating learning science into practices), I had a realization. I frequently see in practice, and it’s pretty much the orientation of the tools, that we work forwards. That is, we start at the beginning, work our way forward through content and practice, and end at, well, the end. While this may make sense from a workflow perspective, there’s a fundamental flaw. So I think it’s time we take a step backward.

Once we’ve done the analysis, and put our goal in mind, it can seem reasonable to move forward, through the various steps. It’s one way to create a coherent experience. However, there’s a flaw with this. For one, it takes our eye off the ball. That is, what’s core is what our performers come out able to do. Getting lost in the flow of experience may lead us astray. For another, it’s assuming we’ll get it right the first time. That’s a mistake.

You can start at either of two places to see an alternative. For one, as McTighe and Wiggins have advocated in Understanding by Design, they focus on the outcomes first and work backwards. For another, modern successors to older design practices – Michael Allen’s Successive Approximations Model (SAM), Megan Torrance’s Lot Like Agile Management Approach (LLAMA), Cathy Moore’s Action Mapping, and David Merrill’s Pebble in the Pond, for some prominent examples – all start designing from the practice first. They iterate on the practice with testing, while working backwards to necessary prerequisite problems, until you get to where your audience starts.

This is both pragmatic and principled. On principle, you work backwards from the core problem. This keeps you aligned with the outcome. Then you supplement with the minimal material to help performers succeed. This includes examples, models, etc. Then, like with a proper paper, you write the introduction and closing last. Pragmatically, this keeps the focus on the critical parts, and ensures you’re focusing your valuable time honing the most important elements first.

It’s easy (trust me, I fall prey to this too) to work forward. Still, it’s smarter to take a step backward and work that way. If you want an impact. Which, I suspect, you do. Or you should, eh?

I’d first heard of Roger Schank’s work as an AI ‘groupie’ during my college years. His contributions to cognitive science have been immense. He was a challenging personality and intellect, and yet he fought for the right things. He passed away yesterday, and he will be missed.

Roger’s work connected story to cognition. He first saw how we had expectations about events owing to his experience at a restaurant with an unusual approach. At Legal Seafoods (at the time) you paid before being served (more like fast food than a sit-down venue). Surprised, Roger realized that there must be cognitive structures for events that were similar to the proposed schemas for things. He investigated the phenomena computationally, advancing artificial intelligence and cognitive science. Roger subsequently applied his thinking to education, writing Engines for Education (amongst other works), while leading a variety of efforts in using technology to support learning. He also railed against AI hype, accurately of course. I was a fan.

I heard Roger speak at a Cog Sci conference I attended to present part of my dissertation research. The controversy around his presentation caused the guest speaker, Stephen Jay Gould, to comment “you guys are weird”! His reputation preceded him; I had one of his PhD graduates on a team and he told me Roger was deliberately tough on them, saying “if you can survive me, you can survive anyone”.

I subsequently met up with Roger at several EdTech events hither and yon. In each he was his fiery, uncompromising self. Yet, he was also right. He was a bit of a contradiction: opinionated and unabashed, but also generous and committed to meaningful change. He also was a prodigious intellect; if you were as smart as him, I guess you had a reason to be self-confident. I got to know him a bit personally at those events, and then when he engaged me for advice to his company. He occasionally would reach out for advice, and always offer the same.

He could be irritating in his deliberate lack of social graces, but he was willing to learn, and had a good heart. In return, I learned a lot from him, and use some of his examples in my presentations. It was an honor to have known him, and the world will be a little duller, and probably a little dumber, without him. Rest in peace.

I just finished reading Ross Dawson’s Thriving on Overload, and it’s a worthy read. The subtitle basically explains it: The 5 powers for success in a world of exponential information. The book has balance between principle and practice, with clear and cogent explanations. It’s not the only model for information management given the increasing challenge, but it’s a worthwhile read if you’re looking for help in coping with information deluge.

I’d heard Ross speak at an event, courtesy of my late friend Jay Cross. Ross is renown as a futurist, perceiving trends ahead of most folks. An Aussie (my 2nd home ;), I can’t say I really know him, but he has a well-established reputation, and keynotes around the world. He was perfectly coherent then and is again here.

Dawson frames elements in terms of how our brain works, which makes sense. He suggests: having an initial purpose, understanding the connections, filtering what’s coming in, paying attention to what’s important, and synthesizing what’s seen. Then, of course, it’s integrating them into a collective whole. He tosses in many interesting and useful observations along the way.

I’ve been, and remain, a fan of Harold Jarche’s Personal Knowledge Management (PKM). His framework is fairly simple – seek, sense, share – though the nuances make it powerful. He receives a mention, but I see some synergies. Harold takes the ‘purpose’ as implicit, and I see Dawson’s framing and synthesizing as both parts of Jarche’s ‘sense’. Similarly, I see Dawson’s attention and filtering as equivalent to Jarche’s ‘seek’. Where they differ most is, to me, where Jarche asks you to share out your learning, and Dawson’s is more personal.

Dawson’s steps are coherent, individually and collectively. As a fan of diagramming, I liked his focus on framing. He grounds much of his arguments in the natural ways our brains work, which I also am a fan of. I will quibble slightly at the end, where he says our brains are evolving to meet this new demand. If we use a metaphor between hardware and software, I’d agree that our brains adapt, but that’s not unique to information overload. What isn’t happening is our brain’s architecture changing. I think his claim maybe slightly misleading in that sense. A small quibble with a generally very good book.

Overall, I think the practices Dawson recommends are valuable and sound. In this era of increasing information, having practices that assist are critical. You can take Harold’s workshop, or read Ross’s book; both will give you useful skills. What you shouldn’t do is continue on without some systematic practices. If you’re looking for help coping with information, it’s available. Recommended.