- Complex learning, step by step
- Complex learning (coffee on the side)
- Ten little steps, and how One grew
- Problem solving, scaffolding, and varied practice
- Step 2: sequencing tasks, or, what next?
- Clusters, chains, and part-task sequencing
- Step 3: performance objectives (the how of the what)
- Criteria for objectives–also, values and attitudes
- Step 4: supportive info (by design)
- Learning to learn (an elaboration)
- Step 5: cognitive strategies (when you don’t know what to do)
- Step 6: (thinking about) mental models
- Step 7: procedural info, or, how to handle routine
- Procedural in practice
- Step 8: cognitive rules, or, when there IS a right way
- Step 9: prerequisites, or, ya gotta start somewhere
- Step 10: part-task practice (getting better at getting faster)
- You? Auto? Practice.
- Media’s role in complex learning
- Self-directed learning: stepping out on your own
- Where do the Ten Steps lead?
To recap briefly, supportive information includes cognitive strategies (different ways that experts go about dealing with problems in a domain) and mental models (the conceptual maps showing how parts of the domain are organized).
This post will summarize the four remaining topics in Step 4:
- Presentation strategies–how to help people learn by providing supporting information
- Elaboration–what it is and why it matters
- Cognitive feedback–another element in learning to learn
- Positioning–when to provide the theory, when to provide feedback
Deduction, induction–your thoughts?
Deduction is hard for novices. I’d say it’s nearly impossible. Since by definition they’re new to the field, they have little if anything already in their mental inventory to connect with the new information.
If you wanted to learn Scottish Gaelic and I began by explaining lenition, slenderization, and epithentic vowels, that’d be taking a deductive approach. Not only would you likely be confused, you couldn’t go into a pub on Lewis and order a drink in Gaelic.
Deductive approaches make sense, the Ten Steps suggest, in certain circumstances:
- Limited time
- Learners already knowledgeable about the domain
- No need for deep knowledge (which certainly applies to my own skill with Scottish Gaelic)
Inductive learning: the specific start
Inductive approaches start with concrete examples and work toward general cases. Where the deductive approach is exposition (“principles of widget design”), the inductive approach is inquisitory (“What do you like about the layout of Amazon’s home page? What do you like about the layout of Zappo’s?”). Examples and models are like stepping stones to the more general concept.
What’s going on is that the specifics help to activate what the learner already knows, since it’s easier to grasp a concrete example than a general case. Using analogies and models, the learner makes connections between what she’s already learned and the new information at hand.
Inductive approaches are inevitably more time-consuming, especially if there’s a great distance between the examples and the broad concept they’re part of. A short-distance example might be seeing photos or videos of different dogs in order to come up with the broad concept of dogs. That’s less challenging that seeing examples of behavior and coming up with the concept of “justice.”
Even so, the Ten Steps advocates making induction the default strategy for complex learning.
Guided discovery: you’re on your own
Guided discovery is a third approach. There’s no presentation; the learner independently identifies and articulates the general information. vM&K make a distinction between pure discovery and guided discovery; the latter has leading questions and other prompts.
You can see how a detailed serious game, simulation, or virtual world could serve as a vehicle for guided discovery–the structure of the environment, the information available, the decisions to make can all provide opportunities for the learner.
When to consider this approach?
- Ample time
- Learners with well-developed discovery skills
- A need for deep understanding
(“Epistemic forms” have to do with how knowledge is organized and how different facts and concepts are related. )
Let’s do more with elaboration
vM&K say (again) that supportive information–those cognitive strategies and mental models–provide a bridge between what learners know already and the new information they’re working with. Along with induction, a key learning process is elaboration.
This means in part that the learner searches his memory for ways to understand and connect with the new information. “Oh, that’s kind of like when I…” This links what he already knows to the new material, and does so consciously.
The theory is that elaboration aids learning because the more connections that exist (within the parts of the new material, and between the new and what’s already known), the more readily he can retrieve and apply what’s new.
Elaboration and induction can produce new cognitive schemas to guide problem-solving and reasoning about a domain. Usually that guidance is specific, as if you’re following a mental checklist. ( “I saw a problem like this five years ago–let me think, that was for an executor who was transferring funds from a 403(b)…”)
Performers sometimes activate certains schemas often enough that they become automatic. vM&K don’t think this is all that common, but it may produce the tacit knowledge we think of as the specialist’s knack.
People constructed advanced schmas through elaboration or induction, and then form cognitive rules of these as a function of direct experience. Afterwards, the schemas quickly become difficult to articulate because they are no longer used as such. The cognitive rules directly drive performance, but are not open to conscious inspection.
To me, this makes sense, and helps explain the notion that it takes around 10,000 hours to become an expert. That amount of time–the equivalent of five years at a full-time job–allows for increase range and depth in a field, and thus for richer elaboration.
Of course, some people don’t have five years’ experience; they’ve got one year that they repeat five times.
Cognitive feedback’s not for correcting
Feedback on the quality of performance, in the Ten Steps, is cognitive feedback. It refers only to the non-recurrent aspects of the tasks, and provides information (such as prompts, cues, and questions) to help the learner construct or reconstruct cognitive schemas so that future performance is improved.
Such feedback encourages the learner to reflect both on the problem-solving process and on the solution found. vM&K say the purpose is not to detect and correct errors but to encourage self-reflection.
This, they say, relates to the concept of double-loop learning put forth by Chris Argyris and summarized in a blog post by Ed Batista.
How can you encourage this?
- Ask learners to compare their own problem-solving processes with those in systematic approaches to problem-solving (SAPs), or with those of other learners, or with mdeling examples.
- Ask learners to compare their own solutions (or partial solutions) with those in case studies, with expert solutions, with those of other learners.
- Provide counter-examples.
In that last case, vM&K give the example of a medical student who decides that a patient has a particular disease based on the particular symptoms. The instructor provied a hypothetical patient who has the same symptioms, some of which may have arisen as side-effects of medication (rather than from the disease in question).
What goes where?
Van Merriënboer and Kirshner suggest that in a deductive approach, general information (the theory) appears in the form of lectures, textbooks, and other preset formats. In an inductive approach, learners often have to search for the theory; they start with the specific examples.
And in a guided discovery approach, the general information is never presented in a ready-made form; the learners must articulate it for themselves.
Meanwhile, cognitive feedback makes no sense until learners have finished a learning task. You can’t see how you did until you’ve done something. (Note, as vM&K do, that immediate feedback does make sense for the recurrent aspects of tasks, as we’ll see in a later post.)