Deeper learning is about the application of learning science to achieve the meaningful outcomes such interventions should deliver. Yet, if you don’t know the subtleties, it can be easy to be swayed by well-produced content. That’s a mistake; small differences actually make a big difference in whether learning will achieve the dual grails of retention and transfer. Without these, learning will not meet the necessary business goals.
Several years ago, four colleagues in the field got together to point out the differences between what constitutes traditional eLearning with what would represent deeper learning, ultimately creating the Serious eLearning Manifesto. In it, they pointed out nuances that matter. Here, we’re revisiting those differences in a slightly different way, to the same end; trying to increase the quality of what is delivered by our industry.
The first-place learning can go wrong is just in characterizing what we’re trying to address. Too often, I see well-intentioned courses focusing on information or knowledge, e.g., awareness. Which would be comprehensible if we were formally logical reasoners. Unfortunately, we’re not, and we don’t change behavior just on the basis of new information.
To make meaningful change, we need to focus on actual performance issues. I usually find this in the form of decisions we need to make differently or better. There are barriers to this, for instance, that our experts have as much as 70% of their underlying thinking inaccessible to conscious awareness; they literally can’t tell us what they do!
Thus, ensuring that our objectives are focused on meaningful outcomes is the first area where we can and should apply deeper learning. We need to have something worthwhile to retain and transfer! We need to ensure that the focus of our efforts is appropriately directed. If you see learning that’s focused on information, not on behaviors, you’re seeing learning that’s not sufficiently deep.
We know that learning proceeds better when we’re emotionally engaged. If we care, we pay more attention, invest more effort, and thus our retention and transfer are improved. Yet too often our introductions to the learning fail to help learners comprehend the relevance of the learning.
When learners understand the personal relevance, the What’s In It For Me (WIIFM), they learn better. Yet, too often, we take for granted that they’ll comprehend the relevance and rationale. That’s not a safe bet. Instead, we should make a concrete effort to ensure that they know they need this. We can do this with the positive consequences of having the knowledge, or the negative consequences of not having the knowledge, but we should do it.
If you see learning that just starts talking about what is to be covered, without addressing the ‘why’, you’re seeing traditional versus deeper learning. Moreover, you’re seeing instruction for which it’s not clear learners will invest true effort into learning.
Too often, we see people talking about ‘content’. That is, information about what’s to be learned. What is a worry is where this content isn’t differentiated by its role in learning. There are specific types of content that serve important, but different, roles in supporting learning. If someone isn’t making the distinctions, you should be wary.
We know, for instance, that providing models gives learners frameworks to use for making the decisions that the objectives specified. Thus, we can use water in pipes as a metaphor to think about electricity. Good models provide a basis to infer the results of decisions, so you can make a choice that achieves the desired outcome. Not providing useful models leaves the learners to infer their own, which frequently can lead to wrong models.
A second critical form of content is examples. Here, we’re taking those models in different situations and showing how to use them in context as a guide to performance. This provides a basis for unpacking what’s critical from what’s ephemeral. Thus, we provide the necessary support for the transfer identified above as a necessary outcome of learning. Since we frequently can’t provide all the necessary situations, we should be choosing examples that support the appropriate inferences for new situations. Yet, too often, we just have a few examples that were told to us or came from materials, instead of explicitly considering the necessarily useful example.
Without a careful choice of models and examples, we’re minimizing the ability of learners to transfer the knowledge appropriately. We also make it harder to fill in gaps from the models when we’ve forgotten part of an approach, undermining retention as well. In both cases, we’re rendering our learning less likely to be a valuable investment.
The final element that separates deeper learning from what we see too frequently is the element of practice. That is, problems or situations where the learner must actively make choices and get feedback afterward. Whether you term such activities as problems, assessments, or practice, what we know is that learners need to practice using the information to develop a persistent ability to apply it after the learning experience. In short, retention depends on sufficient practice.
To use a metaphor, we can’t build muscle overnight; instead, we need to exercise regularly over time. Only so much strengthening happens in any one day. So, too, with learning. The strength of the traces in our brains can only get so much stronger in any one day before we need rest and further strengthening. This suggests that the outcomes of a one-day ‘learning event’ are unlikely to persist unless we continue interventions. As a consequence, proper practice sufficient to develop a new ability to the necessary level is more than just “until they get it right”. Yet, too frequently we see limited practice.
A second problem is what the practice has the learner doing. All too often, we see practice activities focused on retrieving the knowledge, e.g., “This is the definition of that”. Which will lead to an ability to recite information, but not to be able to the use that knowledge where it matters. It’s the difference between asking whether you know the ingredients in a recipe and whether you can make the dish. To get better at using the information to perform better, we need to practice the performance. We need mini-scenarios and branching scenarios at a minimum.
The right type of practice, and sufficient quantities, are what lead to learning that is retained over time and transfers appropriately. This implies a focus on practice, not ‘content’. Yet we too frequently see the ratio between the two emphasizing the content at expense of practice. Again, the nuances matter.
Well-produced learning has introductions, content, and practice that looks elegant. Well-designed and well-produced learning does similarly. However, only the latter has a good chance of leading to meaningful outcomes. If you don’t understand the nuances, it’s easy to be sold a solution that, well, isn’t one.
Learning is a probabilistic game; you can lead a learner to learn, but you can’t make them think (to paraphrase an infamous saying). What good learning design does is increase the likelihood that learning occurs to a very high degree of probability. However, what differentiates effective learning from ‘content with a quiz’ are nuances that are subtle. Unless you’re aware of the differences, you can easily be misled by aesthetics and production values. Not everyone in the organization needs to know, but those who are expending resources on learning solutions do. We intend that this helps you be better equipped. We are committed to deeper learning, and we hope you can understand why. We’re happy to answer any questions.