Achieving transfer – the ability to apply acquired skills in contexts different from those contexts the skills were mastered in – is, arguably, the sine qua non of education. Capturing transfer of knowledge has been addressed by several user modeling and educational data mining approaches (e.g., AFM, PFA, CFA). While similar, these approaches use different underlying structures to model transfer: Q-matrices and T-matrices. In this work, we compare of a more traditional Q-matrix-based method and the relatively new and more complex T-matrix based method. Comparisons suggest that the T-matrix, although demonstrating only marginally better fits, offers a more interpretable and consistent picture of learning transfer.