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Standard webmetrics tools record the IP address of users’ computers, thereby providing fodder for analyses of their geographical location, and for understanding the impact of e-learning and teaching. In this paper, we describe how two web-based educational systems were engineered to collect geo- referenced data. This is followed by a description of joining these data with demographic and educational datasets for the United States, and mapping different datasets using geographic information system (GIS) techniques to visually display their relationships. We conclude with results from statistical analyses of these relationships that highlight areas of significance.
1. U.S. map showing IA visits (darker dots indicate more visits) overlayed with median family income over 1 year. We also examined statistical relationships between the demographic predictors and the number of site visits per location as reported by GA. Due to the high correlation between some of the predictors, only three out of the five were selected, which were population, number of school districts, and per capita income. A negative binomial regression (to handle the skewed data) showed that population, and per capita income were statistically significant predictors of site visits to both the IA and the ELRC, and number of school districts was also a significant predictor for the ELRC. We interpret these results to mean that online visitors to these sites came from, not surprisingly, more densely populated areas. In addition, the relationship with per capita income may be a function of the amount of resources (i.e., computing) available in the local schools and communities.
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Integrated catalog in space
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