Week 13 (Week 4 of Module 3)

This is week 13, the last formal week of the course.

Digital games and education: researching game-based learning.

Here is a take I'd offer you to mull and hopefully challenge and question. I'd hope that you have taken this approach to all of the argument/analysis I have offered you through the course. We don't move forward by agreeing with one another. :)

The nagging question of the worth, value of educational games1 is in focus this week. As I've argued previously there are a lot of artefacts2 that are used in education that have not undergone much scrutiny as to their effectiveness or value. Much is assumed. We have talked about the many long standing practices of education that are reproduced in most schools that had their origins over a hundred years ago but which persist today.

The key question to ask when you look at any research-based paper is what are the questions they are asking: what are their research questions?

One of the problems with the way many of these questions are framed is that they look to see what educational stuff has been learned but it is often phrased in general learning terms, i.e. what has been learned? The short answer to which is heaps. Humans are learning organisms, it's what we do, all the time, non stop3. We don't have an off switch. The better question is to ask what has been learned and to allow all kinds of learning to be picked up, as much as can be discerned. There is a deference to old practices and the imagined learning that occurs in classrooms.

So there is a dilemma here. If we keep some kind of idealised, real learning that is imagined to occur almost exclusively in schools then the game4 is not worth playing. If, however, we step back and ask the simple, anthropological question, what is going on here, we allow into consideration a much broader set of stuff that is being learned that then can be given careful scrutiny.

If, however, these questions are asked within a school context then you immediately limit what gets to count as important. Did the student learn how to balance an equation? No. Ah. Bad software. That is where it stops when that ought to be the beginning point. The question what did the student learn after using a piece of software should not be terminated simply because she/he did not learn what the software was designed to achieve.

To take a slightly different tack on what have become the standard questions in researching software/computer games for educational purposes, I want to draw a little from Daniel Pink's recent book5. He writes:

suppose I’m in the market for a new vacuum cleaner. Ten or fifteen years ago, I’d have had to go into a store, talk to a salesman who was much better informed than I ever could be, and then rely on him to provide the product I needed at a price that was fair. Today, I can solve the vacuum cleaner problem myself. I can go online and check out specs and ratings of various models. I can post a question on my Facebook page and seek recommendations from my friends and my “friends.” Once I’ve settled on a few possibilities, I can compare prices with a few keystrokes. And I can order my choice from the vendor offering the best deal. I don’t need a salesman at all.

Unless I’ve gotten my problem wrong.

After all, my ultimate aim isn’t to acquire a vacuum cleaner. It’s to have clean floors. Maybe my real problem is that the screens on my windows aren’t sufficient to keep out dust, and replacing them with better screens will keep my entire house cleaner when the windows are open. Maybe my problem is that my carpet collects dirt too easily, and a new carpet will obviate the need for me to always be vacuuming. Maybe I shouldn’t buy a vacuum cleaner but instead join a neighborhood cooperative that shares home appliances. Maybe there’s an inexpensive cleaning service with its own equipment that serves my area. Someone who can help me achieve my main goal—clean floors—in a smarter, cheaper way is someone I’ll listen to and perhaps even buy from. If I know my problem, I can likely solve it. If I don’t know my problem, I might need some help finding it.

Thinking carefully about "the problem" is an important part of thinking in and around current research activity in this field.

The same thinking can be usefully deployed to the methods we use to research. They have barely changed over many decades. It seems odd that so much else has changed and yet the way we think about and conduct research has not. This is all at a time when things are changing outside the academy mainly. Terms like big data, machine learning and automation, point to new ways of collecting new kinds of data and doing different kinds of analysis. This ia just a place marker for those of you who may be thinking about doing some research in this space.

de Freitas & Oliver (2006)

Petrina, Feng, & Kim (2008)