JEREMY Y WANG

[article] Scientific American: How a Computer Game Is Reinventing the Science of Expertise

*Warning: The article is more about gaming than the science of expertise.

Chess experts have given cognitive scientists a great model for understanding expertise. The work of cognitive scientists such as Simon, de Groot, Glaser, & Chi have helped us understand some of the basic differences between experts and non-experts. Their methodologies have been extended to other domains as well (such as physics problem-solving and chicken sexing - look it up).

What we know from these studies is that experts literally see the world differently. Well, I should qualify that... Experts perceive things in their domain of expertise differently than non-experts. For example, they are able to "chunk" perceptual information into bigger pieces that allows them to process more information than non-experts. They can also perceive subtle differences in features that others don't.

Think about sitting next to an elderly person struggling to use a computer. They don't see the same things that you see - all they see is a bunch of squares and words and symbols on a fancy TV. You, the expert, see a Twitter feed in a web browser, which just spoiled who got kicked off Top Chef this week.

Here's the key quote from the article for me:

For decades, a different game, chess, has held the exalted position of “the drosophila of cognitive science”—the model organism that scientists could poke and prod to learn what makes experts better than the rest of us. StarCraft 2, however, might be emerging as the rhesus macaque: its added complexity may confound researchers initially, but the answers could ultimately be more telling.

Now, I don't know much about StarCraft 2, but I do remember playing WarCraft back in the day. From what I remember, this game is about developing resources and building an army to defeat your opponent. Based on the description in this article, the goals of StarCraft 2 (SC2) aren't significantly different.

So, what is it about this game that is going to "reinvent" the science of expertise? Unfortunately, the article doesn't tell us much about this besides some possible cognitive activities that are involved. It is quite strong to say that this game with "reinvent" the way we study expertise. That said, I'll take a crack at some of the candidate reasons why SC2 could "reinvent" cognitive science of expertise. 

(1) "Multitasking"

Over and over again, scientists have shown that people are generally really bad at this. It is really difficult to divide attention between two tasks. However, with training, some tasks can be done more automatically. This is likely what is going on with SC2 - expert players are able to do menial tasks really quickly and automatically, and can therefore spend more of their mental effort doing other, more cognitively demanding tasks. I suspect that there is little transfer of whatever small amount of "multitasking" that is going on in SC2.

(2) Log Files

The replay files from SC2 are supposedly a rich source of data. But only if you know what's going on in a game and why a player is doing certain things. Chess games have been recorded for hundreds of years. Just because you have them doesn't mean you understand them, or that they will tell you anything about how humans think.

(3) Development of Expertise

This is probably the most promising offering for SC2 to cognitive science. If we examine log files for the same player over the course of their development, we might gain insight into how expertise develops. We could look at how good players develop and how not-so-good players develop. We could see if there are different courses of development or multiple paths to expertise.

Will videogames "reinvent" our understanding of expertise? Probably not. But they might help us understand how expertise develops and how we can foster that development.

"Philosophy of science is about as useful to scientists as ornithology is to birds."  

~ Richard Feynman

Click here to download:

Keislar & Shulman - The Problem of Discovery.pdf (1.97 MB)

(download)

Click here to download:

Keislar & Shulman - The Problem of Discovery.pdf (1.97 MB)

This paper, by Evan Keislar and Lee Shulman (best known for advancing PCK, among many other things), takes a critical look at what has been considered a hot topic in science education for the past several decades - "discovery learning." Written in 1966 in the book Learning by Discovery: A Critical Appraisal, the authors bring up several issues with "discovery learning" (equated with the more recent "inquiry learning") and the role of teachers, curriculum developers, and education researcher in mediating this concept in relation to education.

A few of important points came to mind in my reading...

First, the authors acknowledge that teachers have a tendency to overgeneralize research findings in relation to discovery learning. If a study suggests that discovery learning is no more effective than direct instruction, some will use this as justification for teaching solely by direct instruction. That said, the conditions under which direct or discovery approaches to teaching and learning occur really matter. Some of the most important factors influencing the type of approach to use are the subject matter, the maturity of the student(s), the prior knowledge and experiences of the student(s), and space and time constraints of the learning environment. Like many debates, there is a false assumption that either discovery or direct instruction is best in all situations; the authors suggest here that it might be the sequence of these methods used along a continuum that provides the best instruction in a given domain.

Curriculuar development deals with the "oughts" of education - what ought we teach, and how ought we teach it. When we choose and describe learning objectives, we need to be aware of the form that they take. On one hand, they can be purely behavioral (ex. The student can add 2- and 3- digit numbers) or they can take an open-ended form (ex. The student will have an appreciation of numbers). The difficulty in taking one approach or the other is that they both have disadvantages. A set of behavioral learning objectives seems to pigeonhole students as learning machines that can churn out the desired outcome. A set of open-ended learning objectives cannot be assessed using traditional tools. I've come across this difficulty working on standards documents, and my experience tells me that it is not easy to find a balance. There is a delicate balance between writing objectives that prescribe specific ways of teaching and learning and those that allow for the improvisation that occurs in the classrooms of seasoned teachers.

Finally, psychological studies of learning must explore what is meant by "discovery." Discovery of what? By whom? For what purpose? Does this happen internally in the learner's mind, or does that even matter (to take a behaviorist approach)? Educational psychologists have theorized and argued about this over the past several decades, and we are far from consensus. That said, there are some promising approaches; the authors mention computer learning environments as a potentially fruitful tool for studying learning - an area that I am currently pursuing.

In sum, a good paper for science educators and education researchers, written by some top minds in the field, on a topic that continues to be negotiated in the science education domain.