Predicting Transfer in a Game-Based Adaptive Instructional System

Transfer, the application of knowledge to new problem-solving situations, and preparation for future learning are often considered among the primary goals of education, but it is difficult to achieve. This paper presents an initial exploration of young children's ability to transfer the specific knowledge and skills that they have learned from a game-based adaptive instructional system to the types of math problems they typically see in school. We describe a regression tree model predicting young children's far transfer performance of specific knowledge and skills following their performance in a game-based adaptive instructional system called My Math Academy. Findings suggest that My Math Academy gameplay empirically supports positive performance on a task that involves elements of both far transfer of learning and preparation for future learning.