In 2006, Kirschner, Sweller, and Clark published a paper titled “Why Minimal Guidance During Instruction Does Not Work: An Analysis of the Failure of Constructivist, Discovery, Problem-Based, Experiential, and Inquiry-Based Teaching” [Kirschner2006]. Its abstract says:
Although unguided or minimally guided instructional approaches are very popular and intuitively appealing…these approaches ignore both the structures that constitute human cognitive architecture and evidence from empirical studies over the past half-century that consistently indicate that minimally guided instruction is less effective and less efficient than instructional approaches that place a strong emphasis on guidance of the student learning process. The advantage of guidance begins to recede only when learners have sufficiently high prior knowledge to provide “internal” guidance.
The paper set off a minor academic firestorm, because beneath the jargon the authors were claiming that inquiry-based learning doesn’t actually work very well. Inquiry-based learning is the practice of allowing learners to ask their own questions, set their own goals, and find their own path through a subject, just as they would when solving problems in real life. It is intuitively appealing, but Kirschner argued that it overloads learners, since it requires them to simultaneously master both a domain’s factual content and its problem-solving strategies.
More specifically, cognitive load theory posits that people have to deal with three things when they’re learning:
Intrinsic load is what people have to keep in mind in order to carry out a learning task. In a programming class, this might be understanding what a variable is, or understanding how assignment in a programming language is different from creating a reference to a cell in a spreadsheet.
Germane load is the (desirable) mental effort required to create linkages between new information and old, which is one of the things that distinguishes learning from memorization. An example might be learning how to loop through a collection in Python.
Extraneous load is everything else that distracts or gets in the way, such as knowing that tabs look like multiple characters but only count as one when indenting Python code.
According to this theory, searching for a solution strategy is an extra burden on top of applying that strategy. We can therefore accelerate learning by giving learners worked examples that show them a problem and a detailed step-by-step solution, followed by a series of faded examples. The first of these presents a nearly-complete use of the same problem-solving strategy just demonstrated, but with a small number of blanks for the learner to fill in. The next problem is also of the same type, but has more blanks, and so on until the learner is asked to solve the entire problem. (The material that isn’t blank is often referred to as scaffolding, since it serves the same purpose as the scaffolding set up temporarily at a building site.)
For example, someone teaching Python might start by explaining this:
# total_length(["red", "green", "blue"]) => 12 def total_length(words): total = 0 for word in words: total += len(word) return total
then ask learners to fill in the blanks in:
# word_lengths(["red", "green", "blue"]) => [3, 5, 4] def word_lengths(words): lengths = ____ for word in words: lengths ____ return lengths
The next problem might be:
# join_all(["red", "green", "blue"]) => "redgreenblue" def join_all(words): result = ____ for ____ in ____: ____ return result
and learners would finally be asked to tackle:
# acronymize(["red", "green", "blue"]) => "RGB" def acronymize(words): ____
Faded examples work because they introduce the problem-solving strategy piece by piece. At each step, learners have one new problem to tackle. As discussed later, this is less intimidating than a blank screen or a blank sheet of paper. It also encourages learners to think about the similarities and differences between various approaches, which helps create the linkages in the mental model that instructors want them to form.
The key to constructing a good faded example is to think about the problem-solving strategy or solution pattern that it is meant to teach. For example, the series of problems are all examples of the accumulator pattern, in which the results of processing items from a collection are repeatedly added to a single variable in some way to create the final result.
Cognitive load theory has been criticized as being unfalsifiable: since there’s no way to tell in advance of an experiment whether something is germane or not, any result can be justified after the fact by labelling things that hurt performance as “extraneous” and things that don’t “germane’’. However, there is no doubt that faded examples are effective.
Research by Mayer and colleagues on the split-attention effect is closely related to cognitive load theory [Mayer2003]. Linguistic and visual input are processed by different parts of the human brain, and linguistic and visual memories are stored separately as well. This means that correlating linguistic, auditory, and visual streams of information takes cognitive effort: when someone reads something while hearing it spoken aloud, their brain can’t help but check that it’s getting the same information on both channels.
Learning is therefore more effective when redundant information is not presented simultaneously in two different channels. For example, people find it harder to learn from a video that has both narration and on-screen captions than from one that has either the narration or the captions but not both.
The key word in the previous paragraph is “redundant”. It turns out that it’s more effective to draw a diagram piece by piece while teaching rather than to present the whole thing at once. If parts of the diagram appear at the same time as things are being said, the two will be correlated in the learner’s memory. Pointing at part of the diagram later is then more likely to trigger recall of what was being said when that part was being drawn.
Another way to use cognitive load theory to construct exercises is called a Parsons Problem. If you are teaching someone to speak a new language, you could ask them a question, and then give them the words they need to answer the question, but in jumbled order. Their task is to put the words in the right order to answer the question grammatically, which frees them from having to think simultaneously about what to say and how to say it.
Similarly, when teaching people to program, you can give them the lines of code they need to solve a problem, and ask them to put them in the right order. This allows them to concentrate on control flow and data dependencies, i.e., on what has to happen before what, without being distracted by variable naming or trying to remember what functions to call.
An earlier section described how people chunk related or correlated information together so that they can fit more into short-term memory. One key finding in cognition research is that experts have more and larger chunks than non-experts, i.e., experts “see” larger patterns, and have more patterns to match things against. This allows them to reason at a higher level, and to search for information more quickly and more accurately.
It is therefore tempting to try to teach patterns directly–in fact, supporting this is one of the reasons programmers have been so enthusiastic about design patterns. In practice, though, pattern catalogs are too large to flick through and too dry to memorize directly. Giving names to a small number of patterns, though, does seem to help with teaching, primarily by giving the learners a richer vocabulary to think and communicate with [Kuittinen2004].
People learn best when they care about the topic and believe they can master it. Neither fact is particularly surprising, but their practical implications have a lot of impact on what we teach, and the order in which we teach it.
First, as noted in Motivation, most people don’t actually want to program: they want to build a website or check on zoning regulations, and programming is just a tax they have to pay along the way. They don’t care how hash tables work, or even that hash tables exist; they just want to know how to process data faster. We therefore have to make sure that everything we teach is useful right away, and conversely that we don’t teach anything just because it’s “fundamental”.
Second, believing that something will be hard to learn is a self-fulfilling prophecy. This is why it’s important not to say that something is easy: if someone who has been told that tries it, and it doesn’t work, they are more likely to become discouraged.
It’s also why installing and configuring software is a much bigger problem for us than experienced programmers like to acknowledge. It isn’t just the time we lose at the start of boot camps as we try to get a Unix shell working on Windows, or set up a version control client on some idiosyncratic Linux distribution.
It isn’t even the unfairness of asking students to debug things that depend on precisely the knowledge they have come to learn, but which they don’t yet have. The real problem is that every such failure reinforces the belief that computing is hard, and that they’d have a better chance of making next Thursday’s deadline at work if they kept doing things the way they always have. For these reasons, we have adopted a “teach most immediately useful first” approach described in Motivation.
It’s very common for programs to count how many things fall into different categories: for example, how many times different colors appear in an image, or how many times different words appear in a paragraph of text.
Create a short example (no more than 10 lines of code) that shows people how to do this, and then create a second example that solves a similar problem in a similar way, but has a couple of blanks for learners to fill in. How did you decide what to fade out? What would the next example in the series be?
Define the audience for your examples. For example, are these beginners who only know some basics programming concepts? Or are these learners with some experience in programming but not in Python?
Show your example to a partner, but do not tell them what level it is intended for. Once they have filled in the blanks, ask them what level they think it is for.
If there are people among the trainees who don’t program at all, make sure that they are in separate groups and ask to the groups to work with that person as a learner to help identify different loads.
Write five or six lines of code that does something useful, jumble them, and ask your partner to put them in order. If you are using an indentation-based language like Python, do not indent any of the lines; if you are using a curly-brace language like Java, do not include any of the curly braces.