ing for help, how much time you spend with people. Are there some people who never come for help, but whose performance indicates they should? You cannot exactly go up to people and say, “what are we doing wrong, why don’t you come for help?” but consider using a quick survey partway through the term to ask students for feedback on the TAs/lab assistants/student helpers as a group.

6. Do not make a big deal about how you are doing so much and working so hard to be inclusive. Bragging about how sensitive you are will backfire and can make people feel singled out. Do it quietly, and trust that people are likely to learn more and enjoy the class more if they are feeling comfortable in the educational setting you have helped create.

I am sure this is not a comprehensive list. What do you do in your classes? What can you add to my list? Post your ideas to the comments. Thanks!

 

Mark Guzdial “Programming Languages Are the Most Powerful, and Least Usable and Learnable User Interfaces” http://bit.ly/1g39mAX March 27, 2014

Andy Ko wrote a recent blog post ( http://bit.ly/1iVxF3A) with an important claim: “Programming languages are the least usable, but most powerful human-computer interfaces ever invented.” Ko argues the “powerful” part with points about expressiveness and political power. He uses HCI design heuristics to show how programming languages have poor usability. Obviously, some people can use programming languages, but too few people and at great effort.

I see that his argument extends to learnability. There are two ways in which programming languages have poor learnability today—( 1) in terms of expectancy-value and ( 2) in terms of social cost.

What is the benefit of a closure? Eugene Wallingford tweeted a great quote the other day:

Educational psychologists measure
the cognitive load ( http://bit.ly/1lSmG0f)
of instruction, which is the effort that a
student makes to learn from instruction.
Every computer scientist can list a bunch
of things which were really hard to learn,
and maybe could not even be imagined to
start, like closures, recursion in your first
course, list comprehensions in Python,
and the type systems in Haskell or Scala.
http://bit.ly/1BMu8QD

Expectancy-value theory (http:// bit.ly/1sctSGD) describes how individuals balance out the value they expect to get from their actions. Educational psychologists talk about how that expectation motivates learning ( http://edcate.co/1iefV80). Students ask themselves, “Can I learn this?” and “Do I want to learn this? Is it worth it?” You do not pursue a degree in music if you do not believe you have musical ability. Even if you love art history, you might not get a degree in it if you do not think it will pay off in a career. Most of us do not learn Dvorak keyboards ( http://bit.ly/1jvaFNC), even though they are provably better than Qwerty, because the perceived costs just are not worth the perceived benefit. The actual costs and benefits do not really play a role here— perception drives motivation to learn.

If you cannot imagine closures, why would you want to learn them? If our programming languages have inscrutable features (that is, high cognitive load to learn them) with indeterminate benefits, why go to the effort? That is low learnability. If students are not convinced they can learn it and they are not convinced of the value, then they do not learn it.

The social cost of going in a new di-
rection. I was at a workshop on CS Edu-
cation recently where a learning scien-
tist talked about a study of physicists
who did their programming in Fortran-
like languages and only used arrays for
all their data structures. Computer sci-
entists in the room saw this as a chal-
lenge. How do we get these physicists
to learn a better language with a bet-
ter design, maybe object-oriented or
functional? How do we get them to use
better data structures? Then one of the
other learning scientists asked, “How
do we know that our way is better Con-
sider the possibility that we’re wrong.”
We computer scientists are always
happy to argue about the value of one
programming paradigm over anoth-
er. But if we think about it from Andy
Ko’s usability perspective, we need to
think about it for specific users and
uses. How do we know that we can
make life better for these Fortran-
using physicists?

What if we convinced some group of these Fortran-using physicists to move to a new language with a new paradigm? Languages do not get used in a vacuum—they get used in a community. We have now cut our target physicists off from the rest of their community. They cannot share code.

They cannot use others’ libraries, tools,
and procedures. The costs of learning a
new language (with new libraries, pro-
cedures, and tools) would likely reduce
productivity enormously. Maybe pro-
ductivity would be greater later. Maybe.
The value is uncertain and in the future,
but the cost is high and immediate.
Maybe we should focus on students
entering the Fortran-using physics
community, and convince them to
learn the new languages. Learning sci-
entists talk about student motivation to
join a “community of practice” (http://
bit.ly/1kDIhFJ). Our hypothetical phys-
ics student wants to join that commu-
nity. They are learning to value what
the community values. Trying to teach
them a new language is saying: “Here,
use this—it’s way better than what the
people you admire use.” The student
response is obvious, “Why should I be-
lieve you How do you know it’s better, if
it’s not what my community uses?”

Solution: Focus on usability. Communities change, and people learn. Even Fortran-using physicists change how they do what they do. The point is that we cannot impose change from the outside, especially when value is uncertain.

The answer to improving both usability and learnability of programming languages is in another HCI dictum: “Know thy users, for they are not you.” We improve the usability and learnability of our programming languages by working with our users, figuring out what they want to do, and help them to do it. Then the value is clear, and the communities will adopt what they see as valuable.

 

Valerie Barr is a professor at Union College, and chair of ACM-W, ACM’s Committee on Women in Computing. Mark Guzdial is a professor at the Georgia Institute of Technology © 2015 ACM 0001-0782/15/03 $15.00