Longitudinally Investigating the Effect of Mathematics Curriculum on Students' Learning: Application of Growth Mixture Modeling

• In Event: 29.076 - Curriculum Studies and Materials in Mathematics

Abstract

The purpose of this study is to investigate the effect of the Connected Mathematics Project (CMP) on students’ learning through the use of longitudinal latent class modeling. Curriculum researchers often employ both classical statistical methods (e.g., repeated measures analysis) and hierarchical linear modeling (HLM) to examine the longitudinal effect of a curriculum on students’ learning. However, such methods do not allow for the examination of heterogeneity of the study population in terms of longitudinal growth trajectories, which often happens to be the case. In fact, it is quite possible that students exhibit different growth patterns. Growth mixture modeling (GMM) allows researchers to estimate the longitudinal effect of curriculum on students’ learning by identifying latent (hidden) classes of growth trajectories.

Authors

• Jinfa Cai, University of Delaware

• John Moyer, Marquette University

• Ning Wang, Widener University

• Dimiter M. Dimitrov, George Mason University