A computer model for learning to teach : proposed categorizations and demonstrated effects
MetadataShow full item record
With the proliferation of new technological alternatives to the traditional classroom, it becomes increasingly important understand the role that innovative technologies play in learning. Computer environments for learning to teach have the potential to be innovative tools that improve the skill and effectiveness of pre-service and in-service teachers. There is a tacit sense in such environments that “realism” is best created through, and associated with, a kind of pictorial literalism. I designed a computer model (the Direct Instruction tool) that, though simple, appears realistic to many users and thus contradicts that sense of literalism. I also propose a theoretical classification of computer representations based on the relationship (or lack thereof) between perceived usefulness or relevance and realism. In this study, I investigate two questions: 1) What are the kinds of claims or insights that respondents generate in relation to using the DI tool to organize their experiences? 2) How do the functionalities of the DI tool fit with or support what respondents see as meaningful? Results indicate that a model can be seen as relevant and useful even if it is not internally consistent. Two major themes that were meaningful to study participants were the simultaneously positive and negative role of “difficulty” in the classroom, and the balance between past performance and future potential. The DI tool seems to promote a shared focus on these themes despite the diversity of past educational experiences among study participants. Responses to this model suggest that extremely abstracted representations of teaching are able to influence the claims and insights of users, affording a glimpse into the internal realities of pre-service teachers. This in turn creates an opportunity to articulate these alternative realities without judgment, describe them with respect, and make them an object of consideration rather than a hidden force. The results of this study contribute to a theory of computer environments for learning to teach that can shape the effective use of these tools in the present, as well as accommodate new models that may be developed as technologies change in the future.