Technological equity

One of the key concerns about the use of technology is whether it is able to serve all populations of people in such a way that it allows them to achieve valuable functionings  (Sen, 1984). I won't go into detail into Sen's argument here, since I am using capability approach as a way to discuss Warschauer's (2008) analysis of the digital divide.

So what does economics have to do with technology? If one were to use Warschauer's (2008) argument that lack of access to information and communication technology (ICT) is a causal factor in impoverishment, then it is clear that technological access impacts economical well-being, and in turn, an individual's quality of life. However, an understanding of access has to include factors other than physical or infrastructural capabilities. For instance the Enhancing Education Through Technology (EETT) report highlights how physical, digital, human and social resources interact in order to address the issue of access. While  the report acknowledges the role of social resources, there was no analysis regarding the forms of support available for students and teachers. Student literacy was also not examined since it was not within the scope of the report. Thus, although the report presented some useful trends, it did not allow us to understand how students and teachers are using ICT or their capabilities. This gap is arguably bridged by Warschauer (2008), who offers evidence of how technology is used unequally by students of low and high socioeconomic status. Volman and van Eck (2001) on the other hand, offer a further nuanced overview of inequity by including a discussion of how technology impacts gender. In contrast to Warschauer, these authors argue for a fine-grained understanding of use of specific ICT applications according to students' approach towards ICT use, their level of participation and outcomes of this participation.

In terms of my model, I have focused primarily on Warschauer (2008), as well as Volman and Eck's (2001) articles as a way of expressing how certain factors can impact access, and in turn one's level of participation, approach and outcomes. At the micro-level, one can examine the teacher-student-object interaction, whereas the expanded aspects of the interactions include other factors that impact these interactions. To date, I have not made changes to the basis of the model since several revisions ago, primarily it serves its function as a theoretical model that explains interactions between actors within the structures of the world.

Virtual worlds

Ah, virtual worlds. The key argument put forth by Steinkuehler & Duncan (2008), Neulight et al (2007) and Hudson & Degast-Kennedy (2009) is that the learning (be it content, dispositions, etc.) gained from virtual worlds is potentially transferable to real life learning situations. Steinkuehler & Duncan (2008) argue that scientific habits of mind may occur in the wild while Neulight et al (2007) demonstrate that designing for virtual worlds can be beneficial for students but remain challenging for learning. Regardless of methodological issues present in the former study, it stands to reason that some players engage in scientific literacies. A bigger question for me is thus who is participating, since it may very well be that only interested players would visit such forums in the first place, as opposed to a classroom environment where students have limited choice in their participation. This is not to say that the students did not enjoy Whyville, although there seemed to be healthy skepticism among the participants in Hudson & Degast-Kennedy's (2009) study.

What does this all mean for educators and researchers interested in using such technologies in the classroom? For me, the big takeaway from these authors is the role of the teacher and curricular design. Neulight et al (2007) highlighted that learning gains are impacted by these two factors, a message that I think is consistent across all the literature read thus far. Nardi and O'Day will certainly agree that in order for effective learning to occur, educator must understand how individual components of a learning ecology interact. To this effect, my model remains unchanged, but I have illustrated the model with problematic components to illustrate why learning gains in the Neulight study were superficial. Through this illustration, I hope to present justification as to why taking into account components of the model is necessary for learning and demonstrate the usefulness of the current model.