Expertise and Instruction

Overview

Teaching: 15 min
Exercises: 30 min

Questions

• What type of instructor is best for novices?

• How are we (as instructors) different from our learners and how does this impact our teaching?

Objectives

• Identify situations where you have an expert blind spot.

• Demonstrate strategies for compensating for your expert blind spot.

• Demonstrate strategies for avoiding demotivating language.

Having previously discussed the transition from novice to competent practitioner, via the formation of a mental model, we now shift our attention to experts. It’s unlikely that you’ll be teaching experts; the expert we want to talk about is you! You might not yet think of yourself as an expert, but chances are you have already advanced to the point where some of these key characteristics – and potential pitfalls – apply to you. We’ll discuss what distinguishes expertise from novices/competent practitioners, how being an expert can make it more difficult to teach novices, and some tools to help instructors identify and overcome these difficulties.

What Makes an Expert?

An earlier topic described a key difference between novices and competent practitioners. Novices lack a mental model, or have only a very incomplete model with limited utility. Competent practitioners have mental models that work well enough for most situations. How are experts different from both of these groups?

What Is An Expert?

1. Name someone that you think is an expert (doesn’t matter what they’re an expert in). As an expert, what makes them special or different from other people?
   OR
2. What is something that you’re an expert in? How does your experience when you’re acting as an expert differ from when you’re not an expert?

This discussion should take about 5 minutes.

The answer is not that experts know more facts (although they almost always do have a lot of knowledge): competent practitioners can memorize a lot of information without any noticeable improvement to their performance. The answer is rather that experts have more connections among pieces of knowledge; more “short-cuts”, if you will.

One way to illustrate this is to model storing knowledge as a graph in which facts are nodes and relationships are arcs. (This is emphatically not how our brains work, but it’s a useful metaphor.) The key difference between experts and competent practitioners is that experts have many more connections among concepts. Their mental models are much more densely connected. Therefore experts can

• see connections between two topics or ideas that no one else can see
• see a single problem in several different ways
• know how to solve a problem, or “what questions to ask”
• jump directly from a problem to its solution because there is a direct link between the two in their mind. Where a competent practitioner would have to reason “A therefore B therefore C therefore D therefore E”, the expert can go from A to E in a single step (“A therefore E”).

We’ll expand on some of these below and how they can manifest in the way you teach.

Connections and Mental Models

The graph model of knowledge explains why helping learners make connections is as important as introducing them to facts. The more connections a fact has to other facts, the more likely the fact is to be remembered. This builds on our earlier idea of mental models - a mental model is (in part) a set of connections or relationships among facts or concepts.

Expertise and Teaching

Because your learners’ mental models will likely be less densely connected than your own, a conclusion that seems obvious to you will not seem that way to your learners. It’s important to explain what you’re doing step-by-step, and how each step leads to the next one.

Another feature of expertise that has important consequences for teaching is the ability of experts to make use of fluid representations. Two ways of thinking about a problem will seem interchangable to an expert, but will not seem that way to a novice. For example, someone with experience using the bash shell will be able to change back and forth between absolute and relative paths with no difficulty and in fact may not even notice they are doing so. A novice learner, however, would be confused by this unexplained use of two different ways of representing a concept.

More Examples of Fluid Representations

• Programming: Referring to an R object like abcde as both “character vectors” and “strings”.
• Programming: Switching among df[,1], df[,'foo'], and df$foo notation when talking about columns in a data.frame.
• Biology: Switching between common species names and Latin names (e.g. “mouse” vs “Mus musculus”).
• Biology: Using both three letter and one letter amino acid codes interchangeably (e.g. Lys = K = Lysine).
• Chemistry: Switching between “Reference material” and “Standard”
• Mathematics: Thinking of things algebraically vs geometrically.
• Navigation: Switching among different routes between two locations.

Fluid Representations

In the Etherpad, give at least one example of a fluid representation that you use in your own work. If you can, also give an example of a fluid representation that might occur in a Carpentry lesson.

Building awareness of how you can represent the same concept in multiple different ways will help you avoid doing so without explanation while teaching.

This discussion should take about 5 minutes.

Experts are also better at diagnosing errors than novices or competent practitioners. If faced with an error message while teaching, an expert will often figure out the cause of the error and develop a solution before a novice has even finished reading the error message. Because of this, it is very important while teaching to be explicit about the process you are using to diagnose and correct errors, even if they seem trivial to you, as they often will.

Diagnosis (Optional)

What is an error message that you encounter frequently in your work? (These are often syntax errors.) Take a few minutes to plan out how you would explain that error message to your learners. Write the error and your explanation in the Etherpad.

This discussion should take about 5 minutes.

Another potential challenge for experts who teach is what we call expert blind spot. Experts are frequently so familiar with their subject that they can no longer imagine what it’s like to not see the world that way - this inability to see things from a non-expert perspective is an expert blind spot and can lead to what’s known as the expertise-reversal effect - experts are often less good at teaching a subject to novices than people with less expertise who still remember what it’s like to have to learn the things. This effect can be overcome with training, but it’s part of the reason world-famous researchers are often poor lecturers.

Blind Spots (Optional)

1. Is there anything you’re learning how to do right now? Can you identify something that you still need to think about, but your teacher can do without thinking about it?
2. Think about the area of expertise you identified for yourself earlier. What could a potential blind spot be?

The challenge of identifying and working around expert blind spots is one reason why we welcome instructors who still identify as “novices”! Someone who is still in the process of learning can be a more effective instructor because they are speaking from their own recent experience.

Dismissive Language

Experts often betray their blind spot by using the word “just” in explanations, as in, “Oh, it’s easy, you just fire up a new virtual machine and then you just install these four patches to Ubuntu and then you just re-write your entire program in a pure functional style—no problem.” This gives learners the very clear signal that the instructor thinks their problem is trivial and that they therefore must be stupid.

With practice, we can change the way we speak to avoid this type of demotivating language and replace it with more positive and motivating word choices.

Changing Your Language

What other words or phrases can have the effect of demotivating learners? What alternatives can we use to express this meaning in a positive and motivational way?

In the Etherpad, make a list of demotivating words/phrases and alternatives.

This discussion should take about 5 minutes.

Solution

Courtney Seiter lists 10 words and phrases that can change a conversation: if, could, yes, together, thank you, choose to, and, because, willing, and the person’s name. These are motivating words and phrases that can shift mindsets. Jason Fried lists several dirty four-letter words: need, must, can’t, easy, just, only, and fast, as well as examples of how they are used to demotivate. Statements like:

• “We really need it.”
• “If we don’t we can’t …”
• “Wouldn’t it be easy if we just did it like that?”
• “Can you try it real fast?” can be perceived as dismissive or demeaning or worse.

Another language choice that can have very positive effects on learner mindset is to ask “What questions do people have?” rather than “Does anyone have any questions?” Asking “Does anyone have any questions?” can create the impression that you hope people don’t have questions, so that you can continue on with the lesson. By asking what questions people have, you are setting up an expectation that people will, indeed, have questions, and that that is normal and expected.

Expert Advantages

In these ways and others, the high connectivity of an expert’s mental model poses challenges while teaching novices. However, that’s not to say that experts can’t be good teachers. Experts can be effective as long as they take the time to identify and correct for their own expert blind spots. You can use some of the exercises we’ve done while preparing to teach to help you overcome these challenges.

Because of their well-connected knowledge, self-aware experts are well-poised to help students make meaningful connections among their knowledge, to confidently turn an error into a learning opportunity, or to explain a complex topic in multiple ways. The important thing is to be aware of blind spots and to try to identify from the learner’s perspective as much as possible.

You Are Not Your Learners

One way to overcome these limitations is by understanding the goals and motivations of your learners. We will discuss motivation in more depth in a later lesson but for now, consider some of these ideas about the typical audience for Carpentry workshops.

• Most scientists’ primary goal is not to program, but to do scientific research. They may not 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”.
• Believing that something will be hard to learn is a self-fulfilling prophecy. Researchers already believe that computing is hard, a belief that is exacerbated by “experts” telling them that things should be easy when, in the researcher’s experience, they’re not. Paradoxically, 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 workshops as we try to get a Unix shell working on Windows, or set up a version control client on some idiosyncratic Linux distribution, or ask people to download and unzip files. It isn’t even the unfairness of asking learners 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 conference submission deadline if they kept doing things the way they always have. For these reasons, we have adopted a “teach most immediately useful first” approach. We’ll talk much more about this when we discuss motivation.

The Carpentries Is Not Computer Science

Many of the foundational concepts of computer science, such as computability, are difficult to learn and not immediately useful. This does not mean that they aren’t important, or aren’t worth learning, but if our aim is to convince people that they can learn this stuff, and that doing so will help them do more science faster, they are less compelling than things like automating repetitive tasks.

The Importance of Practice (Again)

All of the above points illustrate the importance of using formative assessments frequently. The right formative assessment at the right time will give you valuable information about your learners’ goals and motivations, making it easier for you to target your lesson materials to their needs. This strategy also helps you as an instructor overcome your expert blind spot. It doesn’t matter how easy you think a task is, if your learners aren’t getting it, it’s probably more complicated than you thought.

Key Points

• Experts face challenges when teaching novices due to expert blind spot.

• Expert blind spot: knowing something so well that it seems easy when it’s not.

• With practice, we can learn to overcome our expert blind spot.