This week we talked about Virtual Worlds. The content of this image is related primarily to a paper about the spread of the WhyPox outbreak in Whyville (you can see a host of Whyville-related research at http://kafai-whyville.blogspot.com). However, I think that all of the papers that we read this and in the previous unit on games discuss both the power of virtual experiences to situate learning, and also raise a question about how and when that learning moves out of the virtual world.

I’m struggling this week with how to make the full range and scope of a literacy visible to students in the context of computational literacy as inspired by diSessa’s (2000) book, Changing Minds. It seems that most people either “get it” and therefore don’t need a description, or would need quite a bit of exposure to truly grasp the implications. It feels similar to trying to explain to a 5 year-old why literacy is so much more powerful than their understanding the storybook in front of them, long before they have even begun to appreciate all of the powerful ways that the ability to read and write can transform their daily life. This drawing, therefore, attempts to sidestep the issue by focusing on some of the underlying assumptions of what it might mean to reach a point where computational literacy is accessible to all, as Wing (2008) and diSessa (2000) would encourage us to strive for.

This dtg is a response to:
Colella, V. (2000). Participatory simulations: Building collaborative understanding through immersive dynamic modeling. Journal of the Learning Sciences, 9(4), 471-500.

This was inspired by several of this week’s readings that all focused on how various kinds of computational software can make complex science concepts more accessible to students of all ages. Notably:

White, B., & Frederiksen, J. R. (2000). Technological tools and instructional approaches for making scientific inquiry accessible to all. In M. J. Jacobson & R. B. Kozma (Eds.), Innovations in science and mathematics education. Mahwah, NJ: Lawrence Erlbaum Assoc.

Wilensky, U., & Reisman, K. (2006). Thinking Like a Wolf, a Sheep, or a Firefly: Learning Biology Through Constructing and Testing Computational Theories—An Embodied Modeling Approach. Cognition and Instruction, 24(2), 171-209.

Sandoval, W. A., & Reiser, B. J. (2004). Explanation-Driven Inquiry: Integrating Conceptual and Epistemic Scaffolds for Scientific Inquiry. Science Education, 88(3).

Note: the formula on the whiteboard came from the Wikipedia page for Kepler’s laws of planetary motion.

Just saw this video linked on Michael Wesch’s Digital Ethnography Blog which includes a brief discussion of its origins as a class project in Lynn Schofield Clark’s Innovation in Mass Communications class at the University of Denver, and some of the benefits of doing such a project. It’s a really nice framing of a few of the common problems that arise when people try to integrate technology in the classroom, and even has the Office theme song which is always a bonus.

The dtg this week was designed for two purposes. First, I was responding to Koedinger & Corbett (2006) and Schwartz, Biswas, Leelawong & Davis (2007). These two book chapters discuss technologies that were modeled in very different but interesting ways on the success of human tutors and interactions, as well as a rich understanding of the content domain. Second, we have been drawing and refining models of how technology can support educational ecosystems in class. This is my attempt to infer a general model from the two readings so that we can discuss the limitations of my inference and the underlying models in class. My goal is that this will serve as the center for an interesting conversation about both the readings and the modeling process in general.

[Update]

Just a quick note to say that from my perspective this class exercise was an absolute success. My choice to be intentionally vague in the model led the students to some really productive discussions about what nuances were missing as they revisited the readings to critique the models.

Koedinger, K. R., & Corbett, A. (2006). Cognitive Tutors: Technology Bringing Learning Sciences to the Classroom. In K. Sawyer (Ed.), The Cambridge Handbook of the Learning Sciences (pp. 61-78). New York, NY: Cambridge University Press.

Schwartz, D. L., Biswas, K. P. B. G., Leelawong, K., & Davis, J. (2007). Animations of thought: Interactivity in the teachable agent paradigm. In R. Lowe & W. Schnotz (EDS.), Learning.

This is in response to Nardi and O’day (1999) and Burbules and Callister (1999), which we read to help us problematize how we were framing the big issues before diving into specific technologies.

It seems like I can’t stop myself from combining the different theoretical perspectives each week.  A question to ask, though, is whether this combination actually makes sense?  Or does it leave out something key?

[Update]

This representation was originally created for p544. However, in preparing for p574 and thinking about some useful dtg images, I’ve decided to add several tags to this representation so that it fits with both classes.