Many factors may contribute to inter-individual variability in learning, including the mechanics of the particular skill or information that is being learned and the strategies by which the same phenomenon, concept, or principle can be apprehended \cite{KANFER1990221,marton1976qualitative}. It is intuitively plausible that different individuals could be predisposed to engage in different learning strategies based on the nature of their central nervous system \cite{bassett2017network}. Unique genetic and environmental factors present in the early stages of development can give rise to different wiring patterns in the brain \cite{di2014unraveling}, causing different patterns of neural activity in response to stimuli \cite{Atun-Einy2012,harrison2011learning,wigfield2000expectancy}. A particularly parsimonious language in which to describe and characterize such patterns is that of network science \cite{bullmore2009complex,bassett2017networka}, where regions of the brain are represented as network nodes whose activity can vary over time \cite{murphy2016explicitly}, and connections between regions are represented as network edges whose strength can also vary over time \cite{gu2017functional}. While some organizational principles of brain network organization and dynamics appear to be conserved across individuals \cite{betzel2016multi}, others – including measures of activity \cite{prat2011individual,prat2007individual} and connectivity \cite{KANFER1990221} – vary appreciably \cite{Finn2015Functional}. It is unknown to what degree shared versus unique features of brain network structure and dynamics explain the processes of learning and the resultant behavior.