Modest analytics: using the index method to identify students at risk of failure

Abstract

Regression is the tool of choice for developing predictive models of student risk of failure. However, the forecasting literature has demonstrated the predictive equivalence of much simpler methods. We directly compare one simple tabulation technique, the index method, to a linear multiple regression approach for identifying students at risk. The broader purpose is to explore the plausibility of a flexible method that is conducive to adoption and diffusion. In this respect this paper fits within the ambit of the modest computing agenda, and suggests the possibility of a modest analytics. We built both regression and index method models on 2011 student data and applied these to 2012 student data. The index method was comparable in terms of predictive accuracy of student risk. We suggest that the context specificity of learning environments makes the index method a promising tool for educators who want a situated risk algorithm that is flexible and adaptable.