The first task in teaching is to figure out who your learners are and how best to help them. Our approach is based on the Dreyfus model of skill acquisition, and more specifically on the work of researchers like Patricia Benner, who studied how nurses progress from being novices to being experts [Benner2000]. Benner identified five stages of cognitive development that most people go through in a fairly consistent way. (We say “most” and “fairly” because human beings are variable, and there will always be outliers. However, that shouldn’t prevent us from making strong statements about what’s true for the majority.)
For our purposes, we simplify the five stages to three:
A novice is someone who doesn’t know what they don’t know, i.e., they don’t yet know what the key ideas in the domain are or how they relate. They reason by analogy and guesswork, borrowing bits and pieces of their mental models of other domains which seem superficially similar.
A competent practitioner is someone who has a mental model that’s good enough for everyday purposes: they can do normal tasks with normal effort under normal circumstances. This model does not have to be completely accurate in order to be useful: for example, the average driver’s mental model of how a car works probably doesn’t include most of the complexities that a mechanical engineer would be concerned with.
One sign that someone is a novice is that the things they say aren’t even wrong, e.g., they think there’s a difference between programs they type in character by character and identical ones that they have copied and pasted. As we will discuss later, it is very important not to shame novices for this.
One example of a mental model is the ball-and-spring model of molecules that most of us encountered in high school chemistry. Atoms aren’t actually balls, and their bonds aren’t actually springs, but the model does a good job of helping people reason about chemical compounds and their reactions. Another model of an atom has a small central ball (the nucleus) surrounded by orbiting electrons. Again, this model is wrong, but useful for many purposes.
Novices, competent practitioners, and experts need to be taught differently. In particular, presenting novices with a pile of facts early on is counter-productive, because they don’t yet have a model to fit those facts into. In fact, presenting too many facts too soon can actually reinforce the incorrect mental model they’ve cobbled together. As Derek Muller wrote about this [Muller2011] in the context of video instruction for science students:
Students have existing ideas about scientific phenomena before viewing a video. If the video presents scientific concepts in a clear, well illustrated way, students believe they are learning but they do not engage with the media on a deep enough level to realize that what was is presented differs from their prior knowledge.
There is hope, however. Presenting students’ common misconceptions in a video alongside the scientific concepts has been shown to increase learning by increasing the amount of mental effort students expend while watching it.
The goal with novices is therefore to help them construct a working mental model so that they have somewhere to put facts. As an example of what this means in practice, Software Carpentry’s lesson on the Unix shell introduces fifteen commands in three hours. Twelve minutes per command may seem glacially slow, but the lesson’s real purpose isn’t to teach those fifteen commands: it’s to teach learners about paths, history, tab completion, wildcards, pipes and filters, command-line arguments, redirection, and all the other big ideas that the shell depends on. Once they understand those concepts, people can quickly learn a repertoire of commands. What’s more, later lessons on how to build functions in a programming language can refer back to pipes and filters, which helps solidify both ideas.
Different Kinds of Lessons
The cognitive differences between novices and competent practitioners underpin the differences between two kinds of teaching materials. A tutorial’s purpose is to help newcomers to a field build a mental model; a manual’s role, on the other hand, is to help competent practitioners fill in the gaps in their knowledge. Tutorials frustrate competent practitioners because they move too slowly and say things that are obvious (though of course they are anything but to newcomers). Equally, manuals frustrate novices because they use jargon and don’t explain things. One of the reasons Unix and C became popular is that Kernighan et al’s trilogy [Kernighan1982], [Kernighan1984], [Kernighan1988] somehow managed to be good tutorials and good manuals at the same time. Ray and Ray’s book on Unix [Ray2014] and Fehily’s introduction to SQL [Fehily2008] are among the very few other books in computing that have accomplished this.
One of the challenges in building a mental model is to clear away things that don’t belong. As Mark Twain said, “It ain’t what you don’t know that gets you into trouble. It’s what you know for sure that just ain’t so.”
Broadly speaking, learners’ misconceptions fall into three categories:
Simple factual errors, such as believing that Vancouver is the capital of British Columbia (it’s Victoria). These are simple to correct, but getting the facts right is not enough on its own.
Broken models, such as believing that motion and acceleration must be in the same direction. We can address these by having them reason through examples to see contradictions.
Fundamental beliefs, such as “the world is only a few thousand years old” or “some kinds of people are just naturally better at programming than others” [Patitsas2016]. These are often deeply connected to the learner’s social identity, and so are resistant to evidence and cannot be reasoned away in class.
Teaching is most effective when instructors have a way to identify and clear up learners’ misconceptions while they are teaching. The technical term for this is formative assessment, which is assessment that takes place during the lesson in order to form or shape it. Learners don’t pass or fail formative assessments; instead, its main purpose is to tell both the instructor and the learner how the learner is doing, and what to focus on next. For example, a music teacher might ask a student to play a scale very slowly in order to see whether she is breathing correctly, and if she is not, what she should change.
The counterpoint to formative assessment is summative assessment, which is used at the end of the lesson to tell whether the desired learning took place and whether the learner is ready to move on. One example is a driving exam, which reassures the rest of society that someone can safely be allowed on the road.
*When the cook tastes the soup, that’s formative. when the guests taste the soup, that’s summative.
- Michael Scriven, as quoted by Debra Dirksen.
Connecting Formative and Summative Assessment
One rule to use when designing lessons is that formative assessments should prepare people for summative assessments: no one should ever encounter a question on an exam for which the teaching did not prepare them. This doesn’t mean that novel problems should not appear, but that if they do, learners should have had practice with and feedback on tackling novel problems beforehand.
In order to be useful during teaching, a formative assessment has to be quick to administer and give an unambiguous result. The most widely used kind of formative assessment is probably the multiple choice question (MCQ). When designed well, these can do much more than just tell whether someone knows something or not. For example, suppose we are teaching children multi-digit addition. A well-designed MCQ would be:
Q: what is 27 + 15 ?
The correct answer is 42, but each of the other answers provides valuable insight:
If the child answers 32, she is throwing away the carry completely.
If she answers 312, she knows that she can’t just discard the carried 1, but doesn’t understand that it’s actually a ten and needs to be added into the next column. In other words, she is treating each column of numbers as unconnected to its neighbors.
If she answers 33 then she knows she has to carry the 1, but is carrying it back into the same column it came from.
Each of these incorrect answers is a plausible distractor with diagnostic power. “Plausible” means that it looks like it could be right, while “diagnostic power” means that each of the distractors helps the instructor figure out what to explain to that particular learner next.
A good MCQ tests for conceptual misunderstanding rather than simple factual knowledge. If you are having a hard time coming up with diagnostic distractors, then either you need to think more about your learners’ mental models, or your question simply isn’t a good starting point for an MCQ.
When you are trying to come up with distractors, think about questions that learners asked or problems they had the last time you taught this subject. If you haven’t taught it before, think about your own misconceptions or ask colleagues about their experiences. You can also ask open-ended questions in one class to collect misconceptions about material to be covered in a later class.
Instructors will often put supposedly-silly answers like “a fish!” on MCQs, particularly ones intended for younger learners. However, they don’t provide any insight into learners’ misconceptions, and most learners don’t actually find them funny.
Instructors should use MCQs or some other kind of formative assessment every 10-15 minutes in order to make sure that the class is actually learning. That way, if a significant number of people have fallen behind, only a short portion of the lesson will have to be repeated. Additionally, most learners can only focus intensely for roughly this long, so using formative assessments this frequently also helps them re-focus.
Formative assessments can also be used preemptively: if you start a class with an MCQ and everyone can answer it correctly, then you can safely skip the part of the lecture in which you were going to explain something that your learners already know. Doing this also helps show learners that the instructor cares about how much they are learning, and respects their time enough not to waste it.
But what should you do if most of the class votes for one of the wrong answers? What if the votes are evenly spread between options? The answer is, “It depends.” If the majority of the class votes for a single wrong answer, you should go back and work on correcting that particular misconception. If answers are pretty evenly split between options, learners are probably guessing randomly and it’s a good idea to go back to a point where everyone was on the same page.
If most of the class votes for the right answer, but a few vote for wrong ones, you have to decide whether you should spend time getting the minority caught up, or whether it’s more important to keep the majority engaged. This is just one example of one of the most important rules of teaching: no matter how hard you work, or what teaching practices you use, you won’t always be able to give everyone the help they need.
Given enough data, MCQs can be made surprisingly precise. The best-known example is the Force Concept Inventory, which gauges understanding of basic Newtonian mechanics. By interviewing a large number of respondents, correlating their misconceptions with patterns of right and wrong answers to questions, and then improving the questions, its creators constructed a diagnostic tool to pinpoint specific misconceptions. However, it’s very costly to do this, and students’ ability to search for answers on the internet is an ever-increasing threat to the validity of tools like this.
Designing an MCQ with plausible distractors is useful even if it is never used in class because it forces the instructor to think about the learners’ mental models and how they might be broken–in short, to put themselves into the learners’ heads and see the topic from their point of view.
Why Not MOOCs
Massive open online courses (MOOCs) in which students watch videos instead of attending lectures, and then do assignments that are (usually) robo-graded, were a hot topic a few years ago. Now that the hype has worn off, though, it’s clear that they aren’t as effective as their more enthusiastic proponents claimed they would be [Ubell2017].
Recorded content is ineffective for most novices learners because it cannot intervene to clear up specific learners’ misconceptions. Some people happen to already have the right conceptual categories for a subject, or happen to form them correctly early on; these are the ones who stick with most massive online courses, but many discussions of the effectiveness of such courses ignore this survivor bias.
If you use robots to teach, you teach people to be robots.
– variously attributed
If you haven’t done so already, you should start using these three teaching practices in your instructor training workshop:
Use sticky notes as status flags so that you can quickly see who needs help, who has questions, and who’s ready to move on.
Use sticky notes to distribute attention so that everyone gets a fair share of the instructor’s time.
Use sticky notes as minute cards to encourage learners to reflect on what they’ve just learned and to give instructors actionable feedback while they are still in a position to act on it.
What is one mental model you use to frame and understand your work? Write a few sentences describing it in the shared notes, and give feedback on other learners’ contributions.
What are the symptoms of being a novice? I.e., what does someone do or say that leads you to classify them as a novice in some domain?
Create a multiple choice question related to a topic you intend to teach and explain the diagnostic power of each its distractors (i.e., what misconception each distractor is meant to identify).
When you are done, give your MCQ to a partner, and have a look at theirs. Is the question ambiguous? Are the misconceptions plausible? Do the distractors actually test for them? Are any likely misconceptions not tested for?
A good formative assessment requires people to think through a problem. For example, consider this question from [Epstein2002]. Imagine that you have placed a cake of ice in a bathtub and then filled the tub to the rim with water. When the ice melts, does the water level go up (so that the tub overflows), go down, or stay the same?
The correct answer is that the level stays the same: the ice displaces its own weight in water, so it exactly fills the “hole” it has made when it melts. Figuring this out why helps people build a model of the relationship between weight, volume, and density.
Describe another kind of formative assessment you have seen or used and explain how it helps both the instructor and the learner figure out where they are and what they need to do next.