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Archives for February 2023

Misconceptions?

28 February 2023 by Clark 5 Comments

Several books ago, I was asked to to talk about myths in our industry. I ended up addressing myths, superstitions, and misconceptions. While the myths persist, the misconceptions propagate, aided by marketing hype. They may not be as damaging, but they also are a money-sink, and contribute to the lack of our industry making progress. How do we address them?

The distinctions I make for the 3 categories are, I think, pretty clear. Myths are beliefs that folks will willingly proclaim, but are contrary to research. This includes learning styles, attention span of a goldfish, millennials/generations, and more (references in this PDF, if you care). Superstitions are beliefs that don’t get explicit support, but manifest in the work we do. For example, that new information will lead to behavior change. We may not even be aware of the problems with these! The last category is misconceptions. They’re nuanced, and there are times when they make sense, and times they don’t.

The problem with the latter category is that folks will eagerly adopt, or avoid, these topics without understanding the nuances. They may miss opportunities to leverage the benefits, or perhaps more worrying, they’ll spend on an incompletely-understood premise. In the book, I covered 16 of them:

70:20:10
Microlearning
Problem-Based Learning
7 – 38 – 55
Kirkpatrick
NeuroX/BrainX
Social Learning
UnLearning
Brainstorming
Gamification
Meta-Learning
Humor in Learning
mLearning
The Experience API
Bloom’s Taxonomy
Learning Management Systems

On reflection, I might move ‘unlearning’ to myths, but I’d certainly add to this list. Concepts like immersive learning, workflow learning, and Learning Experience Platforms (LXPs)  are some that are touted without clarity. As a consequence, people can be spending money without necessarily achieving any real outputs. To be clear, there are real value in these concepts, just not in all conceptions thereof. The labels themselves can be misleading!

In several of my roles, I’m working to address these, but the open question is “how?” How can we illuminate the necessary understanding in ways that penetrate the hype? I truly do not know. I’ve written here and spoken and written elsewhere on previous concepts, to little impact (microlearning continues to be touted without clarity, for instance). At this point, I’m open to suggestions. Perhaps, like with myths, it’s just persistent messaging and ongoing education. However, not being known for my patience (a flaw in my character ;), I’d welcome any other ideas!

Thinking artificially

21 February 2023 by Clark Leave a Comment

I finally put my mitts on ChatGPT. The recent revelations, concern, and general plethora of blather about it made me think I should at least take it for a spin around the block. Not surprisingly, it disappointed. Still, it got me thinking about thinking artificially. It also led me to a personal commitment.

What we’re seeing is a two-fold architecture. On one side is a communication engine, e.g. ChatGPT. It’s been trained to be able to frame, and reframe, text communication. On the other side, however, must be a knowledge engine, e.g. something to talk about. The current instantiation used the internet. That’s the current problem!

So, when I asked about myself, the AI accurately posited two of my books. It also posited one that as far as I know, doesn’t exist! Such results are not unknown. For instance, owing to the prevalence of the learning styles myth (despite the research), the AI can write about L&D and mention styles as a necessary consideration. Tsk!

The problem’s compounded by the fact that many potential knowledge bases, beyond the internet, have legacy problems. Bias has been a problem in human interactions, and records thereof can also therefore have bias. As I (with co-author Markus Bernhardt) have opined, there is a role for AI in L&D, but a primary one is ensuring that there’s good content for an AI engine to operate on. Another, I argue, is to create the meaningful practice that AI currently can’t, and is likely true for the foreseeable future. I also have yet to see an AI that can create a diagram (tho’ that, to me, isn’t as far-fetched, depending on the input).

I have heard from colleagues who find the existing ChatGPT very valuable. However, they don’t take what it says as gospel, instead they use it as a thinking partner. That is, they’ll prompt it with thoughts they’re having to see what comes up. The goal is to get some lateral input to consider (not take as gospel). It’s a way to consider ideas they may have missed or not seen, which is a valuable role.

At this point, I may or may not use AI in this way, as a thinking (artificially) partner. I’ll have to experiment. One thing I can confidently assert is that everything you read (e.g. here) that is truly from me (i.e. there’s the possibility I will be faked ) will be truly from me. I’m immodest enough to think that my writing is not in need of artificial enhancement. I may be wrong, but that’s OK with me. I hope it is with you, too!

A step backward?

14 February 2023 by Clark Leave a Comment

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?

It’s complex

7 February 2023 by Clark Leave a Comment

In a recent conversation, I was talking about good design. Someone asked a question, and I elaborated that there was more to consider. Pressed again, I expanded yet more. I realized that when talking good learning design, it’s complex. However, knowing how it’s complex is a first step. Also, there are good guidelines. Still, we will have to test.

I’m not alone in suggesting that, arguably, the most complex thing in the known universe is the human brain. I jokingly ask whether bullet points are going to lead to sustained changes in behavior in such a complex organism? Yet, I also tout learning science design principles that help us. Is there a resolution?

The complexity comes from a number of different issues. For one, the type, quantity, challenge, and timing of practice depends on multiple factors. Things that can play a role include how complex the task is, how frequently it’s performed, and how important the consequences are. Similarly, the nature of the topic, whether it’s evolutionarily primary or secondary can also have an influence. The audience, of course, makes a difference, as does the context of practice. Addressing the ‘conative’ element – motivation, anxiety, confidence – also require some consideration.That’s a lot of factors!

Yet, we know what makes good practice, and we can make initial estimates of how much we need. Likewise, we can choose a suite of contexts to be covered to support appropriate transfer. We have processes as well as principles to assist us in making an initial design.

Importantly, we should not assume that the first design is sufficient. We do, unfortunately, and wrongly. Owing to the complexity of items identified previously, even with great principles and practices, we should expect that we’ll need to tune the experience. We need to prototype, test, and refine. We also need to build that testing into our timelines and budgets.

There is good guidance about testing, as well. We know we should focus on practice first, using the lowest technology possible. We should test early and often. Just as we have design guidance, these are practices that we know assist in iterating to a sufficient solution. Similarly, we know enough that it shouldn’t take much tuning since we should be starting from a good basis.

Using the cognitive and learning sciences, we have good bases to start from on the way to successful performance interventions. We have practices that address our limitations as designers, and the necessities for tuning. We do have to put these in practice in our planning, resourcing, and executing. Yet we can create successful initiatives reliably and repeatedly if we follow what’s known, including tuning. It’s complex, but it’s doable. That’s the knowledge we need to acknowledge, and ensure we possess and apply.

Vale Roger Schank

3 February 2023 by Clark 4 Comments

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.

Coping with information

2 February 2023 by Clark Leave a Comment

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.

 

Clark Quinn

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