An important step at the start of course development is analysing the target group, which among other things gives us information about the learners’ prior level of knowledge. When choosing the method to apply, it matters a great deal whether we are dealing with novices, intermediate learners, or experts with deep familiarity with the subject. In this article we look at how learners at these different levels differ, and at how those differences should shape our methodological decisions.

Who counts as an expert?

To begin with, it is important to clarify who we regard as an expert. They are the people who have extensive knowledge and organised memory structures — schemas — in a given field. Applying these schemas, they think effectively and are good problem solvers.

Their other characteristics include:

  • Experts notice patterns and information that escape a novice’s attention.
  • Their knowledge cannot be reduced to a set of isolated facts or propositions; it forms a coherent, complex system. The applicability of their knowledge is determined by the context of the particular problem.
  • They can bring their prior knowledge to bear almost automatically when they need it. They do not have better memories — it only appears that way because of the schemas they have built.
  • Although experts know their field thoroughly, this does not guarantee that they will be able to teach others.
  • Because experts aim to understand problems rather than immediately attempting a solution, they can sometimes appear slower than their less experienced colleagues.

Two popular beliefs fall apart as a result. There is no such thing as an independent skill called problem solving: it can only be understood within a given discipline, and it is a product of expert knowledge. Nor does the idea hold up that with the arrival of the internet we no longer need to learn any data, facts, formulae and so on, since we can always look them up. Information can only be applied if it is part of a complex system of knowledge.

How do novices and experts differ?

Now that we can see what an expert is like, let us talk a little about novices. Many experiments have examined the differences between a novice and someone with deep familiarity with a subject. These experiments showed that experts’ knowledge differs not only in quantity but also in quality.

Let us see what that means in practice. An expert begins the problem-solving process by activating their existing schemas, recalling earlier experience and similar problems already solved. With a word problem, they evaluate, categorise and contextualise the problem while still reading the task. It follows that the difference lies not only in the speed of problem solving but also in how it is done. Experts see the deeper relationships and information, while novices typically notice only surface features. Such surface features might be, in a physics problem, that the task is about moving objects — while the difference between velocity and acceleration as physical concepts has not yet settled. Schemas exist, but they are not yet formed, or are even inaccurate, so they do not necessarily help towards the solution. Novice learners may remember the formulae for calculating velocity and acceleration, but they cannot yet use them confidently and correctly in every case in practice.

It follows from all this that novices are not small experts, any more than children are small adults. Our methods have to take these differences into account. Driving is a favourite example in writing about learning, so let me draw an analogy from there too. A novice driver has much of their mental capacity taken up by coordinating their limbs while driving and watching the road and the traffic at the same time, so starting them off drifting on a wet racetrack is unlikely to help. An experienced driver on an advanced driving course, by contrast, has their focus somewhere else entirely, and would only find it irritating if we went over releasing the clutch in a stationary car.

The expertise reversal effect

This phenomenon is called the expertise reversal effect. It means that the effectiveness of the chosen method is significantly affected by the learner’s prior level of knowledge. So what is good and necessary for a novice is ineffective, or even harmful, for an expert — and vice versa.

A good example is the method of discovery learning, where acquiring knowledge is based on exploring each topic individually and thoroughly. The idea rests on the proposition that knowledge acquired independently brings a sense of achievement, and thereby greater engagement and far more durably retained knowledge. But this only works for learners who already have broader knowledge of the field. For novices it is decidedly harmful, because our working memory — where new information is processed — is limited. All we do with a novice is block them with too much information; the method will not motivate them if they do not even know how to begin, or what counts as important information and what does not.

For an expert, on the other hand, material pitched at a much lower level of knowledge can create noise and overload working memory, as they have to pick out the new knowledge they want from information that is already superfluous to them. In both cases the problem is cognitive overflow; the difference lies in the source of the cognitive load. For novices it is caused by too much new information, for experts by too much redundant knowledge. In other words, once mental schemas have formed, the method needed to build those schemas can itself become an obstacle to learning.

This post has shown why analysing the target group’s prior level of knowledge matters so much before deciding on methodological principles. Not only will we miss the goal if we choose badly — we may even do harm. While a novice needs far more guidance and structure in the learning process, an expert must be given a great deal of freedom and space in order to learn effectively. As e-learning professionals we must always keep this in mind.

Author: Mariann Tóth