Mirjam Neelen & Paul A. Kirschner
Most of us agree that the workplace (and thus jobs) is changing rapidly and that this has consequences for both workers and employers. Interestingly, this agreement evaporates when it comes to what the consequences of those changes are, such as what skills people need to ACQUIRE and how they should acquire them.
Something that looks like a sure bet is that cognitive tasks that must be carried out by people will become increasingly complex (also see our blog on complex skills). It’s also likely that these cognitively-complex task-focused skills will include problem-solving, critical thinking, and decision-making. To achieve this, complex learning is called for. The tricky bit here is that, when dealing with complex learning, the whole is more than the sum of its part and the question is: How do we design effective, efficient, and enjoyable learning experiences for this?
This blog is based on a conversation between Mirjam and Prof.dr Jeroen van Merriënboer, research program director of the Graduate School of Health Professions Education (SHE) at Maastricht University in the Netherlands with a chair in Learning and Instruction. He’s famous for the four-component instructional design model (4C/ID), which focuses on acquisition and training of complex cognitive skills. You can read the whole interview in our book Evidence-Informed Learning Design.
What is complex learning in the workplace?
There are different definitions of complex learning. My own definition of complex learning originally was based on an analysis of learning processes. This analysis makes a difference between schema construction processes where people construct cognitive schemas and schema automation processes where people automate cognitive schemas. Then there’s a subdivision. When we look at schema construction, people can construct new schemas by connecting new information with what they already know. That’s called elaboration. People can also construct new schemas based on concrete experiences. That’s what we call inductive learning. When it comes to schema automation, we can make a similar distinction around sub processes. One sub process is typically called rules formation. Those are about rules that are frequently used and then become cognitive rules. When these schemas are applied many, many times, so when there’s a lot of practice, these rules become more and more strengthened.
So basically, you can describe learning in terms of elaboration, inductive learning, rule formation, and strengthening of rules. When all these processes occur simultaneously, that’s when I call it complex learning. So, it’s a combination of different learning processes.
There’s also a simpler definition, that distinguishes between three types of learning. The first is declarative learning, which is about learning knowledge, procedural learning, which is about learning skills, and affective learning, which is about attitudes. Complex learning then entails an integration of these three domains. Combining these is what we see in competence-based approaches of learning, for example, in the medical domain.
Why is it so important to take a whole-task approach when you’re dealing with complex learning tasks?
A whole task approach drives complex learning. When people are performing an authentic professional task, these tasks always almost make an appeal on knowledge, skills, and attitudes. Or, I could also say that these tasks always make an appeal on schema construction and schema automation processes. So, whole tasks are a way to ‘force’ learners into this process of complex learning. The desirable effect of this is that the transfer of learning increases. Basically, as a result of complex learning, people develop integrated knowledge structures that also enable them to perform new tasks in new situations.
When we don’t take a whole task approach and only teach knowledge, for example, or only skills, it’s extremely difficult for people to apply this knowledge or these skills in new situations. That’s because they’re simply not able to coordinate these knowledge, skills, and attitudes in the new situation because they weren’t taught in an integrated manner. You’ll just end up with fragmented schemas that don’t come together.
It’s simply all about preparing people for professional tasks that are new and unfamiliar. They must have an integrated knowledge base that enables them to successfully approach these tasks.
In the workplace, there’s a lot of moving parts that can get in the way to design learning experiences with a whole task approach. Things like, time, budget, stakeholder opinions, but sometimes also a lack of knowledge and skills. How would you explain to learning professionals and stakeholders in the workplace why they should do it?
Well, the transfer of learning is all about flexibility and adaptive expertise. It’s about being able to be creative, to come up with new solutions to new problems. A whole task approach to complex learning can help organizations to become more innovative. It can help organizations to have a workforce that’s creative, prepared to develop new approaches, and overcome new challenges that the organization is facing. If an organization is looking for that type of workforce, a whole task approach to complex learning is extremely important to bring a company further.
It’s also a matter of accepting that it’s useless to distinguish between what’s happening in the workplace and what’s happening in a training context. It should be one thing. It’s quite common for organizations to distinguish between workplace learning and training and it doesn’t make any sense.
There’s also a big misconception that people need to understand basic concepts before they can start to practice authentic tasks. I think people really start to understand basic concepts by application, by using the concepts. The concepts develop through inductive learning. The basic concepts are extremely important, but I disagree that there should be an order, like understanding the concepts first and then applying them. It’s integrated. Your understanding also develops as a result of practicing. This is precisely what you see in 4C/ID. The work on the learning tasks and the development of knowledge go hand in hand. Supportive information and the learning tasks are connected to each other. It’s going back and forth between applying the concepts in your learning tasks and studying the supportive information. It doesn’t make sense to say that one comes before the other. Knowledge will only be retained when it’s developed in the process of the actual task application.
The 4C/ID model might be a bit intimidating for people at first. What can learning professionals do to start considering learning experiences that involve an integrated set of learning goals, focusing on whole tasks? What could be a starting point?
The backbone of the training design is always the professional task identification. Don’t start with a list of learning objectives. That’s what’s typically done in training. That’s almost a guarantee that you end up with fragmented training that doesn’t have an impact. So, starting with an analysis around what professionals actually do and identifying these authentic professional tasks, and using these tasks as a basis for training design is probably the most important principle to follow. This is probably a big shift for many learning designers in organizations because they’re so used to working with these objectives, or even the content that needs to be covered, and not real-life tasks.
What, in your experience, are the challenges that come with training development projects?
One of the main challenges is always accepting that training development takes time. Another challenge is to compose the team that’s responsible for the training development to make sure that all the different types of expertise are represented. You simply can’t design training without experts. Finding the experts with the required domain knowledge is really difficult sometimes.
From a technical point of view, training design following the 4C/ID model isn’t that difficult, however, implementing it in an organization is actually very difficult.
Actually, it’s important to not explain the model in detail but refer to best practices instead. It works way better for people to look at examples of training programs that have used the 4C/ID model than me explaining the abstract model. That approach is also consistent with the 4C/ID model. People learn more from examples than the more abstract and general principles. After studying the examples, they actually suddenly understand the abstract model. There are really good examples in medical, nursing, and software engineering, but there might also be domains of which I’m not aware.
We’re not ‘one of the worst kind’ so, even though they are not part of the conversation Mirjam had with Van Merriënboer, here are some scientific articles with case studies on the 4C/ID model:
- Information problem solving program (Higher Ed)
- Communication skills (Professionals)
- Programming and logical reasoning (Secondary Ed)
What is part of our book, is Van Merriënboer sharing how he worked on real-life projects dealing with complex learning. If you want to read practical examples from the complex learning master himself, you can find them in our book ‘Evidence-Informed Learning Design’ 😊.
 Schema construction is a category of learning processes responsible for constructing cognitive schemas which might then be interpreted by controlled processes to generate behavior in new, unfamiliar situations. Sub processes are inductive learning and elaboration.
 Schema automation is a category of learning processes responsible for automating cognitive schemas which, then, contain cognitive rules that directly steer behavior without the need for conscious control. Sub processes are rule formation and strengthening.
 Elaboration is a category of learning processes by which learners connect new information elements to each other and to knowledge already available in long-term memory. Elaboration is a form of schema construction that is especially important for the learning of supportive information using, for example, multimedia, hypermedia, and social media.
 Rule formation is a category of learning processes by which learners embed new information in cognitive rules that directly steer behavior. Rule formation is a form of schema automation especially important for learning procedural information.
 Adaptive expertise requires an individual to develop conceptual understanding that allows the expert to invent new solutions to problems and even new procedures for solving problems. See: https://en.wikipedia.org/wiki/Adaptive_expertise