Learners need encouragement to step out into unfamiliar terrain, so this chapter discusses ways instructors can motivate them. More importantly, it discusses ways that we can accidentally demotivate them, and how we can avoid doing that.
People learn best when they care about the topic and believe they can master it. This presents us with a problem because most people don’t actually want to program: they want to make music or compare changes to zoning laws with family incomes, and rightly regard programming as a tax they have to pay in order to do so. In addition, their early experiences with programming are often demoralizing, and believing that something will be hard to learn is a self-fulfilling prophecy.
Imagine a grid whose axes are labelled “mean time to master” and “usefulness once mastered”. Everything that’s quick to master, and immediately useful should be taught first; things in the opposite corner that are hard to learn and have little near-term application don’t belong in this course.
Any useful estimate of how long something takes to master must take into account how frequent failures are and how much time is lost to them. For example, editing a text file seems like a simple task, but most graphical editors save things to the user’s desktop or home directory. If people need to run shell commands on the files they’ve edited, a substantial fraction won’t be able to navigate to the right directory without help. If this seems like a small problem to you, please revisit the discussion of expert blind spot in Memory.
Many of the foundational concepts of computer science, such as computability, inhabit the “useful but hard to learn” corner of the grid described above. This doesn’t mean that they 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.
We therefore recommend a “teach most immediately useful first” approach. Have learners do something that they think is useful in their daily work within a few minutes of starting each lesson. This not only motivates them, it also helps build their confidence in us, so that if it takes longer to get to the payoff of a later topic, they’ll stick with us.
The best-studied use of this idea is the media computation approach developed by Guzdial and Ericson at Georgia Tech [Guzdial2013]. Instead of printing “hello world” or summing the first ten integers, their students’ first program opens an image, resizes it to create a thumbnail, and saves the result. This is an authentic task, i.e., something that learners believe they would actually do in real life. It is also has a tangible artifact: if the image comes out the wrong size, learners have a concrete starting point for debugging.
Strategies for Motivating Learners
[Ambrose2010] contains a list of evidence-based methods to motivate learners. None of them are surprising–it’s hard to imagine someone saying that we shouldn’t identify and reward what we value–but it’s useful to check lessons against these points to make sure they’re doing at least a few of these things.
What’s missing from this list is strategies to motivate the instructor. Learners respond to an instructor’s enthusiasm, and instructors need to care about a topic in order to keep teaching it, particularly when they are volunteers.
Women aren’t leaving computing because they don’t know what it’s like; they’re leaving because they do know.
– variously attributed
If you are teaching free-range learners, they are probably already motivated–if they weren’t, they wouldn’t be in your classroom. The challenge is therefore not to demotivate them. Unfortunately, we can do this by accident much more easily than you might think.
The three most powerful demotivators are unpredictability, indifference, and unfairness. Unpredictability demotivates people because if there’s no reliable connection between what they do and what outcome they achieve, there’s no reason for them to try to do anything. If learners believe that the instructor or the educational system doesn’t care about them or the lesson, they won’t care either. And if people believe the class is unfair, they will also be demotivated, even if it is unfair in their favor (because consciously or unconsciously they will worry that they will some day find themselves in the group on the losing end [Wilkinson2011]). In extreme situations, learners may develop learned helplessness: when repeatedly subjected to negative feedback that they have no way to escape, they may learn not to even try to escape when they could.
Here are some quick ways to demotivate your learners:
A “holier-than-thou” or contemptuous attitude from an instructor.
Tell learners they are rubbish because they use Excel and/or Word, don’t modularize their code, etc.
Repeatedly make digs about Windows and praise Linux, e.g., say that the former is for amateurs.
Criticize GUI applications (and by implication their users) and describe command-line tools as the One True Way.
Dive into complex or detailed technical discussion with the one or two people in the audience who clearly don’t actually need to be there.
Pretend to know more than you do. People will actually trust you more if you are frank about the limitations of your knowledge, and will be more likely to ask questions and seek help.
Use the J word (“just”). As discussed in Memory, this signals to the learner that the instructor thinks their problem is trivial and by extension that they therefore must be stupid for not being able to figure it out.
Feign surprise. Saying things like “I can’t believe you don’t know X” or “you’ve never heard of Y?” signals to the learner that they do not have some required pre-knowledge of the material you are teaching, that they are in the wrong place, and it may prevent them from asking questions in the future.
Code of Conduct Revisited
As noted at the start, we believe very strongly that classes should have a Code of Conduct. Its details are important, but the most important thing about it is that it exists: knowing that we have rules tells people a great deal about our values and about what kind of learning experience they can expect.
Never Learn Alone
One way to support learners who have been subject to systematic exclusion or discrimination (overt or otherwise) is to have people sign up for workshops in small teams rather than as individuals. If an entire lab group comes, or if attendees are drawn from the same (or closely-related) disciplines, everyone in the room will know in advance that they will be with at least a few people they trust, which increases the chances of them actually coming. It also helps after the workshop: if people come with their friends or colleagues, they can work together to implement what they’ve learned.
Impostor syndrome is the belief that one is not good enough for a job or position, that one’s achievements are lucky flukes, and an accompanying fear of being “found out”. Impostor syndrome seems to be particularly common among high achievers who undertake publicly visible work.
Academic work is frequently undertaken alone or in small groups but the results are shared and criticized publicly. In addition, we rarely see the struggles of others, only their finished work, which can feed the belief that everyone else finds it easy. Women and minority groups who already feel additional pressure to prove themselves in some settings may be particularly affected.
Two ways of dealing with your own impostor syndrome are:
Ask for feedback from someone you respect and trust. Ask them for their honest thoughts on your strengths and achievements, and commit to believing them.
Look for role models. Who do you know who presents as confident and capable? Think about how they conduct themselves. What lessons can you learn from them? What habits can you borrow? (Remember, they quite possibly also feel as if they are making it up as they go.)
As an instructor, you can help people with their impostor syndrome by sharing stories of mistakes that you have made or things you struggled to learn. This reassures the class that it’s OK to find topics hard. Being open with the group makes it easier to build trust and make students confident to ask questions. (Live coding is great for this: typos let the class see you’re not superhuman.)
You can also emphasize that you want questions: you are not succeeding as a teacher if no one can follow your class, so you’re asking students for their help to help you learn and improve. Remember, it’s much more important to be smart than to look smart.
The Ada Initiative has some excellent resources for teaching about and dealing with imposter syndrome [Ada2017].
Reminding people of negative stereotypes, even in subtle ways, makes them anxious about the risk of confirming those stereotypes, which in turn reduces their performance. This is called stereotype threat, and the clearest examples in computing are gender-related. Depending on whose numbers you trust, only 12-18% of programmers are women, and those figures have actually been getting worse over the last 20 years. There are many reasons for this (see [Margolis2003] and [Margolis2010]), and [Steele2011] summarizes what we know about stereotype threat in general and presents some strategies for mitigating it in the classroom.
However, while there’s lots of evidence that unwelcoming climates demotivate members of under-represented groups, it’s not clear that stereotype threat is the underlying mechanism. Part of the problem is that the term has been used in many ways; another is questions about the replicability of key studies. What is clear is that we need to avoid thinking in terms of a deficit model (i.e., we need to change the members of under-represented groups because they have some deficit, such as lack of prior experience) and instead use a systems approach (i.e., we need to change the system because it produces these disparities).
A great example of how stereotypes work in general was presented in Patitsas et al’s “Evidence That Computer Science Grades Are Not Bimodal” [Patitsas2016]. This thought-provoking paper showed that people see evidence for a “geek gene” where none exists. As the paper’s abstract says:
Although it has never been rigorously demonstrated, there is a common belief that CS grades are bimodal. We statistically analyzed 778 distributions of final course grades from a large research university, and found only 5.8% of the distributions passed tests of multimodality. We then devised a psychology experiment to understand why CS educators believe their grades to be bimodal. We showed 53 CS professors a series of histograms displaying ambiguous distributions and asked them to categorize the distributions. A random half of participants were primed to think about the fact that CS grades are commonly thought to be bimodal; these participants were more likely to label ambiguous distributions as “bimodal”. Participants were also more likely to label distributions as bimodal if they believed that some students are innately predisposed to do better at CS. These results suggest that bimodal grades are instructional folklore in CS, caused by confirmation bias and instructor beliefs about their students.
It’s easy to use language that suggests that some people are natural programmers and others aren’t, but Mark Guzdial has called this belief the biggest myth about teaching computer science.
Learners can be demotivated in subtler ways as well. For example, Dweck and others have studied the differences of fixed mindset and growth mindset. If people believe that competence in some area is intrinsic (i.e., that you either “have the gene” for it or you don’t), everyone does worse, including the supposedly advantaged. The reason is that if they don’t get it at first, they figure they just don’t have that aptitude, which biases future performance. On the other hand, if people believe that a skill is learned and can be improved, they do better on average.
A person’s mindset can be shaped by subtle cues. For example, if a child is told, “You did a good job, you must be very smart,” they are likely to develop a fixed mindset. If on the other hand they are told, “You did a good job, you must have worked very hard,” they are likely to develop a growth mindset, and subsequently achieve more. Studies have also shown that the simple action of telling learners about the different mindsets before a course can improve learning outcomes for the whole group.
As with stereotype threat, there are concerns that research on grown mindset has been oversold, or will be much more difficult to put into practice than its more enthusiastic advocates have implied. While some people interpret this back and forth of claim and counter-claim as evidence than education research isn’t reliable, what it really shows is that anything involving human subjects is both subtle and difficult.
Not providing equal access to lessons and exercises is about as demotivating as it gets. The older Software Carpentry lessons, for example, the text beside the slides includes all of the narration–but none of the Python source code. Someone using a screen reader would therefore be able to hear what was being said about the program, but wouldn’t know what the program actually was.
While it may not be possible to accommodate everyone’s needs, it is possible to get a good working structure in place without any specific knowledge of what specific disabilities people might have. Having at least some accommodations prepared in advance also makes it clear that hosts and instructors care enough to have thought about problems in advance, and that any additional concerns are likely to be addressed.
It Helps Everyone
Curb cuts (the small sloped ramps joining a sidewalk to the street) were originally created to make it easier for the physically disabled to move around, but proved to be equally helpful to people with strollers and grocery carts. Similarly, steps taken to make lessons more accessible to people with various disabilities also help everyone else. Proper captioning of images, for example, doesn’t just give screen readers something to say: it also makes the images more findable by exposing their content to search engines.
The first and most important step in making lessons accessible is to involve people with disabilities in decision-making: the slogan nihil de nobis, sine nobis (literally, “nothing about us, without us”) predates accessibility rights, but is always the right place to start. A few other recommendations are:
Find out what you need to do. The W3C Accessibility Initiative’s checklist for presentations [W3C2017] is a good starting point focused primarily on assisting the visually impaired, while Liz Henry’s blog post about accessibility at conferences [Henry2014] has a good checklist for people with mobility issues, and this interview with Chad Taylor is a good introduction to issues faced by the hearing impaired [Taylor2014].
Know how well you’re doing. For example, sites like WebAIM allow you to check how accessible your online materials are to visually impaired users.
Don’t do everything at once. We don’t ask learners in our workshops to adopt all our best practices or tools in one go, but instead to work things in gradually at whatever rate they can manage. Similarly, try to build in accessibility habits when preparing for workshops by adding something new each time.
Do the easy things first. There are plenty of ways to make workshops more accessible that are both easy and don’t create extra cognitive load for anyone: font choices, general text size, checking in advance that your room is accessible via an elevator or ramp, etc.
Inclusivity is a policy of including people who might otherwise be excluded or marginalized. In computing, it means making a positive effort to be more welcoming to women, people of color, people with various sexual orientations, the elderly, the physically challenged, the formerly incarcerated, the economically disadvantaged, and everyone else who doesn’t fit Silicon Valley’s white/Asian male demographic. Lee’s paper “What can I do today to create a more inclusive community in CS?” [Lee2017] is a brief, practical guide to doing that with references to the research literature. These help learners who belong to one or more marginalized or excluded groups, but help motivate everyone else as well; while they are phrased in terms of term-long courses, many can be applied in our workshops:
Ask learners to email you before the workshop to explain how they believe the training could help them achieve their goals.
Review notes to make sure they are free from gendered pronouns, that they include culturally diverse names, etc.
Emphasize that what matters is the rate at which they are learning, not the advantages or disadvantages they had when they started.
Encourage pair programming.
Actively mitigate behavior that some learners may find intimidating, e.g., use of jargon or “questions” that are actually asked to display knowledge.
Think about something you did this week that uses one or more of the skills you teach, (e.g., wrote a function, bulk downloaded data, did some stats in R, forked a repo) and explain how you would use it (or a simplified version of it) as an exercise or example in class.
Pair up with your neighbor and decide where this exercise fits on a 2x2 grid of “short/long time to master” and “low/high usefulness”? In the shared notes, write the task and where it fits on the grid. As a group, discuss how these relate back to the “teach most immediately useful first” approach.
Pick one activity or change in practice from Lee’s paper [Lee2017] that you would like to work on. Put a reminder in your calendar three months in the future to self-check whether you have done something about it.
Think back to a programming course (or any other) that you took in the past, and identify one thing the instructor did that demotivated you, and describe what could have been done afterward to correct the situation.
Pair up with your neighbor and discuss your stories, then add your comments to the shared notes.
Review the comments in the shared notes as a group. Rather than read them all out loud, highlight and discuss a few of the things that could have been done differently. This will give everyone some confidence in how to handle these situations in the future.
Think back to a time when you demotivated a student (or when you were demotivated as a student). Pair up with your neighbor and discuss what you could have done differently in the situation, and then share the story and what could have been done in the group notes.
Find the nearest public transportation drop-off point to your building and walk from there to your office and then to the nearest washroom, making notes about things you think would be difficult for someone with mobility issues. Now borrow a wheelchair and repeat the journey. How complete was your list of challenges? And did you notice that the first sentence in this challenge assumed you could actually walk?
In [Littky2004], Kenneth Wesson wrote, “If poor inner-city children consistently outscored children from wealthy suburban homes on standardized tests, is anyone naive enough to believe that we would still insist on using these tests as indicators of success?” Read [Cottrill2016], and then describe an example from your own experience of “objective” assessments that reinforced the status quo.