ANALYSIS OF INDIVIDUAL STUDENT DATA

This chapter discusses several models of individual performance in the first-grade mathematics curriculum. Regression techniques were used to examine two basic types of models: a temporal model in which the prediction of an individual's performance for a given block of lessons was based on his performance for the immediately preceding blocks, and a conceptual model in which the prediction was based on performance in previous blocks of lessons of the same concept. Two performance measures were used: the proportion of problems the student answered correctly on the first response and the student's average response latency to the first response. The chapter presents the studies of models of the sort that are of interest for several reasons. The models studied in this chapter concentrate on data from individual students. Second, there has been a tendency in the recent educational literature to claim that social and economic variables are more important in predicting student behavior than academic variables. Third, by using models of the type studied, one can ask questions of the kind that have become fashionable in discussions of cognitive style—questions about whether student variation is greater than curriculum variation.