Module 43: Data Mining for Classification and Regression
Data mining methods for classification and regression are becoming increasingly popular in various scientific fields. However, these methods have not been explored much in educational measurement. This module first provides a review, which should be accessible to a wide audience in education measurement, of some of these methods. The module then demonstrates using three real-data examples that these methods may lead to an improvement over traditionally used methods such as linear and logistic regression in educational measurement.
Keywords: bagging, boosting, classification and regression tree, CART, cross-validation error, data mining, predicted values, random forests, supervised learning, test error, TIMSS