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Clark Quinn’s Learnings about Learning

Archives for January 2023

Hyping the news

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I just saw another of these ‘n things you must…if you…’ headlines, and as usual it had the opposite effect they intended. I guess I’m a contrarian, because such headlines to me are an immediate warning. It happened to be in an area I know about, and I hadn’t done any of the necessary things. Yet, I have done the thing they were saying needed the prerequisites. Arguably well (do awards count?). It made me reflect on how we’re hyping the news. Some thoughts…

Yes, I know that such headlines are clickbait. ‘n‘ should be small. Yet when I tried to boil down Upside’s ‘deeper learning’ list for an infographic, it came to 14 items.  Inconvenient for hype,  I’m afraid, but what I’d put in the white paper. Of course there’s more, but I’m trying to be comprehensive, not ‘attractive’.  Similarly, when I created my EEA alignment, I had nine elements. Not because they were convenient for marketing, but because that’s what emerged from the work.

I similarly see lists for ‘the five things’, or the ‘8 things’ (somehow 8 seems to be a maximum, at least for marketing ;). What worries me about these lists is if they’re comprehensive. Is that really all? Have you ensured that they’re necessary and sufficient? Did you even have a process? It took four of us working through months to come up with the eight elements of the Serious eLearning Manifesto.  None of the above lists (Manifesto, EEA, deeper learning) are definitive, but they are the result of substantial work and thinking. Not just pulled together for a marketing push.

There are good lists, don’t get me wrong. Ones where people have worked to try to identify critical elements, or good choices based upon principled grounds. Typically, if it’s the case, there are pointers to the basis for these claims. Either there’s someone who’s known for work in the area, or they’re transparent about process. However, there are also lists where it’s clear someone’s just pulled together some random bits. Look for inconsistency, mismatches of types, etc.

In the broader picture, it’s clear that generating fear and outrage and sensationalism sell. I just want to demonstrate a resistance, and prefer a clear argument over a rant. (Here I’m trying to do the former, not the latter. ;) This goes with probably my broader prescription: I do want policy wonks making decisions. I really don’t want simple wrong answers to complicated questions no matter how appealing.

So, my short take is if you know the area, read with a critical eye. If you don’t, look for warning signs, and see what those who do know have to say about it. Caveat emptor. That’s my take on trying to stay immune to the hyping of news.

Learners as learning evaluators

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Many years ago, I led the learning design of an online course on speaking to the media. It was way ahead of the times in a business sense; people weren’t paying for online learning. Still, there were some clever design factors in it. I’ve lifted one to new purposes, but also have a thought about how it could be improved. So here are some thoughts on learners as learning evaluators.

The challenge is the result of two conflicting challenges. For one, we want to support free answers on the part of learners. This is for situations where there’s more than one way to respond. For example a code solution, or a proposed social response. The other is the desire for auto-marking, that is independent asynchronous learning. While it’s ideal to have an instructor in the loop to provide feedback, the asynchronous part means that’s hard to arrange. We could try to have an intelligent programmed response (c.f. artificial intelligence), but those can be difficult to develop and costly. Is there another solution?

One alternative, occasionally seen, is to have the learner evaluate their response. There are positive benefits to this, as it gets learners to become self-evaluators. One of the mechanisms to support this is to provide a model answer to compare to the learners’ own response. We did this in that long-ago project, where learners could speak their response to a question, then listen to theirs and a model response.

There are some constraints on doing this; learners have to be able to see (or hear) their response in conjunction with the model response. I’ve seen circumstances where learners respond to complex questions and get the answer, but they don’t have a basis to compare. That is, they don’t get to see their own response, and the response was complex enough not to be completely remembered. One particular instance of this is in multiple response choices where you pick a collection out.

I want to go further, however. I don’t assume that learners will be able to effectively compare their response to the model response. At least, initially. As they gain expertise, they should, but early on they may not have the requisite support. You can annotate the model answer with the underlying thinking, but there’s another option.

I’m considering the value of having an extra rubric that states what you should notice about the model answer and prompts you to see if you have all the elements. I’m suggesting that this extra support, while it might add some cognitive load to the process, also reduces the load by supporting attention to the important aspects. Also, this is scaffolding that can be gradually removed, allowing learners to internalize the thinking.

I think we can have learners as learning evaluators, if we support the process appropriately. We shouldn’t assume that ability, at least initially, but we can support it. I’m not aware of research on this, though I certainly don’t doubt it. If you do know of some, please do point me to it! If you don’t, please conduct it! :D Seriously, I welcome your thoughts, comments, issues, etc.

Debating debates

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This is the year, at the LDA, of unpacking thinking (the broader view of my previous ‘exposure‘). The idea is to find ways to dig a bit into the underlying rationale for decisions, to show the issues and choices that underly design decisions. How to do that? Last year we had the You Oughta Know series of interviews with folks who represent some important ideas. This year we’re trying something new, using debates to show tradeoffs. Is this a good idea? Here’s the case, debating debates.

First, showing underlying thinking is helpful. For one, you can look at Alan Schoenfeld’s work on showing his thinking as portrayed in Collins & Brown’s Cognitive Apprenticeship. Similarly, the benefits are clear in the worked examples research of John Sweller. While it’s fine to see the results, if you’re trying to internalize the thinking, having it made explicit is helpful.

Debates are a tried and tested approach to issues. They require folks to explore both sides. Even if there’s already a reconciliation, I feel, it’s worth it to have the debate to unpack the thinking behind the positions. Then, the resolution comes from an informed position.

Moreover, they can be fun! As I recalled here, in an earlier debate, we agreed to that end. Similarly, in some of the debates I had with Will Thalheimer (e.g. here), we deliberately were a bit over-the-top in our discussions. The intent is to continue to pursue the fun as well as exposing thinking. It is part of the brand, after all ;).

As always, we can end up being wrong. However, we believe it’s better to err on the side of principled steps. We’ll find out. So that’s the result of debating debates. What positions would you put up?

Don’t make me learn!

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In a conversation with a client, the book Don’t Make Me Think was mentioned. Though I haven’t read it, I’m aware of its topic: usability. The underlying premise also is familiar: make interfaces that use pre-existing knowledge and satisficing solutions. (NB: I used to teach interface design, having studied under one of the gurus.) However, in the context of the conversation, it made me also ponder a related topic: “don’t make me learn”. Which, of course, prompted some reflection.

There are times, I’ll posit, when we don’t want employees to be learning. There are times when learning doesn’t make sense. For instance, if the performance opportunities are infrequent, it may not make sense to try to have it in people’s heads. If there’s a resource people can use to solve the problem rather, than learning, that is probably a better answer. That is, in almost any instance, if the information can be in the world, perhaps it should.

One reason for this is learning, done properly, is hard. If a solution must be ‘in the head’ – available when needed and transferring to appropriate situations – there’ll likely be a fair bit of practice required. If it’s complex, much more so. Van Merriënboer’s Four Component Instructional Design is necessarily rigorous! Thus, we shouldn’t be training unless it absolutely, positively, has to be in the head when needed (such as in life-threatening situations such as aviation and medicine).

I’m gently pushing the idea that we should avoid learning as much as possible! Make the situation solvable in some other way. When people talk about ‘workflow learning’, they say that if it takes you out of the workflow, it’s not workflow. I’ll suggest that if it doesn’t, it’s not learning. Ok, so I’m being a bit provocative, but too often we err on the side of throwing training at it, even when it’s not the best solution. Let’s aim for the reverse, finding other solutions first. Turn to job aids or community (learning can be facilitated around either, as well), but stop developing learning as a default.

So, don’t make me learn, unless I have to. Fair enough?

Looking ahead

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A number of people are indicating that 2022 is another year to move on from. And, of course, we do need to move on (as if there were an alternative ;). Still, 2022 was a good year for Quinnovation, and here’s hoping that continues.  Here’re some random thoughts looking ahead.

For one, I saw an interesting piece leveraging the financial adage (really: caution) that “past performance is not indicative of future results”. That comes with various investment opportunities; just because they’ve done well in the past doesn’t meant that will continue. The nice twist in the article was to apply it to yourself: if the past year wasn’t a great one, that doesn’t mean you’re going to continue to suffer. Things can get better despite what happened in the past (or worse), though of course taking your own proactive steps is recommended. Indeed, given that for me, 2020 and 2021 were slow years didn’t mean 2022 had to be. Fortunately!

In the broader sense, I think that despite some hiccups, we’re seeing positive trends. For instance, I increasingly see calls for greater attention to evidence-based practices. While that doesn’t mean it’s happening yet, but the notice is hopefully precedes implementation!

We’ve still some legacies slowing us down, of course. I do think that the belief in us as formal reasoning beings will continue to be a barrier. Still, the above clarion call should help us move (however slowly) to right that wrong.

I’m optimistic, by nature (despite being skeptical). Thus, I think we are working our way forward. I reckon I’ll keep working on that, at least. I am continuing with the Learning Development Accelerator, and Upside Learning, as well of course continuing to do Quinnovative things. I’m looking ahead to us having an impact, together!