Definition of Technology Literacy
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Putting it all together
In researching who would be most affected by the digital divide, what I found was that the poor are more affected than the wealthy, that residents of first world countries are less affected than those in developing nations, that age plays a factor, as does race, and that often the characteristics that indicate people at risk of being caught on the wrong side of the divide overlap a great deal. As well, factors that aren't inherent in one segment of the population may still play a role. For example, a parent caught in the so-called 'grey divide' is less likely to model the enthusiasm and computer usage necessary to impart its importance on their children. These children may grow up being affected by the 'grey divide' even though they themselves aren't a part of that age group.
Overall, the least affected by the digital divide would be white American men with a minimum of a college degree but likely a graduate or professional degree, who earn more than $45,000 a year and live in or near an urban area. In an interesting side note, this is an accurate descriptor of the majority of the top echelon of administrators (Presidents, Vice Presidents, Deans and Provosts) at many colleges and universities in the United States. This may go a long way to explaining why the problems inherent in spanning the digital divide in student populations are largely ignored in favour of an attitude that suggests that all young people, by virtue of being young, automatically have complete technological fluency.
By looking at socioeconomic factors, race, age, gender, ability and global access, administrators should now be able to identify student populations most at risk, and in doing so, be able to intervene with those that need the most help and support. Minority students, students from central urban neighbourhoods, students from less financially advantaged backgrounds, students with disibilities - these are all students at risk of being technologically illiterate, and students who fall into more than one category are exponentially at risk.