Moderation and Prediction of Student Behavior in STMath by Motivation and Environment

In this project we broadly ask whether student behavior in an online, math-learning game, Spatial Temporal (ST) Math, can be predicted by students’ environments and whether this relation is moderated by motivation for mathematics. The motivation variables of interest are expectancy and value, as defined by usefulness and importance, and the environments of interest are home and class. All data are secondary data and were collected during the 2018-2019 school year from one district in California. For analysis, we will run multiple hierarchical linear models (HLMs) to find which explains the most variance; we choose to use HLMs due to the nested nature of the data such that one student may play many levels of ST Math and students have different teachers. Ray Patt and Allison Zengilowski served as primary researchers on this project, with Dr. Teomara Rutherford and Dr. Kerry Duck as advisors.