Speaker 1: In the last video, we explored the motivational path of gamification, that is, tactics that drive the learner forward through your training. Today, we'll look at the path of cognitive engagement, and then we'll compare the two so that you know which way to go for your gamified training. The cognitive path is about giving the learner different ways of engaging mentally with your content, and often more directly. It sounds a little woo-woo, so here's an example. Imagine a game made to help players discover the principles of baking. In this game, you decide the ingredients you put in, and their quantities, and how long you bake them for, and at how high a temperature. And depending on the choices you make, you get anything from a fantastic cake to a sloppy mess, and maybe you even get a score on the combo of choices you make. I don't know, I'm making this up. What's happening here in this baking simulator is that we're letting the player try different things and see what happens. If they fail to achieve what they wanted, they can form a theory about what went wrong, and then they'll try again differently and test that theory. And eventually, they'll infer what the perfect combination is. First thing I want you to notice here, this game is made for this content, baking. It would be hard to use the same mechanics to teach motorcycle maintenance or introduction to Swedish. So here, the training content is not interchangeable. Last week, we saw that with motivational mechanics, you can often switch out the content and use the same mechanics. But with cognitive mechanics, they're much more closely tied to the content. That means that in this category, you won't see as many all-purpose, i.e. reusable mechanics. The mechanics used here are as numerous and various as all the types of content out there that you could ever want to teach, so practically limitless. That said, in the wide world of cognitive mechanics, you will see some recurring themes. For example, a lot of these programs are made to guide the player to experience the content themselves, like with this lab simulation. As they get better and better at performing the procedures, your simulation can give the learners less and less guidance. Similar to this, there are mechanics that will often be designed to let the player explore different alternatives and solve problems through trial and error, like in a branching scenario. You're not just told you're right or wrong, but you get to see the consequences, good or bad, of your decisions. So you're failing safely and constructively. But one useful thing I want to point out here. Notice that the trial and error process is individualized here, meaning everyone's going to bring their own ideas to your content, and everyone can test out their ideas to either confirm or adjust them. In other words, if a player already knows that fish and blueberries aren't a great combo in a cake, they won't need to waste their training time trying it or being told that. Everyone goes through their own path, long or short, to the right combination. So that makes the training time more efficient. And finally, sometimes these mechanics are just about giving the learner opportunities to practice a skill, like in a typing game, so they can build up accuracy and speed. Here the learner already knows the principles, they know how to type, but they need that repetition in order to build up speed. And so the game is giving them that. So while the first category of mechanics is more about using rewards and incentives to motivate the learner, this one is about getting them to experience the content directly. It's about hands-on experience and gaining insight through guided discovery. This helps learners gain a deeper understanding of your content. So how do they compare with each other? Well, first of all, it's not one or the other. In a lot of gamified programs that have cognitive mechanics at their core, you'll find motivational mechanics layered on top. So the two types of elements can happily coexist in the same program. In fact, this is often desirable since different players may be engaged by different elements. Also, the two categories overlap somewhat. If a cognitive mechanic allows you to figure something out, that aha moment you get is deeply satisfying. So a cognitive mechanic can be very motivating. And sometimes a motivational mechanic, for example, an unlock structure, can help the learner see the relationships between the different parts of your content more clearly. But of course, the big question is, which one's better? Well, neither one magically works, and there are good and bad examples of each. Both require careful thought to do well. However, from a production standpoint, the motivational mechanics are often easier to implement. They still require attention and love to do well, though. But because the barrier to entry is usually lower with these mechanics, then a lot of the gimmicky or carelessly made programs out there tend to be motivational in nature. And that gives the whole motivational path a bad rap sometimes. But if there's a key thing to take away from all this, it's that both are aimed at completely different goals. So which one's better really depends on what your content and your learners need. If you're teaching a complex system with lots of variables and you want the learner to understand the cause and effect relationships in that system, then maybe you'll want to build them a simple simulation of it and have them play with it, and gradually you can increase the complexity. If, on the other hand, your content has a lot of rote memorization that needs a great deal of repetition and practice to master, like a vocabulary game, then maybe your first priority will be to motivate the learners to increase their practice time so that they can build up that speedy recall. The upshot is that to use gamification effectively, you first need to figure out what kind of boost you're after, and that'll tell you which category of mechanic you should be looking at first. And knowing the strengths of each category will help you narrow that down and choose the right mechanics. So happy designing, thank you for watching, and I'll see you next time.