Centrality in student interaction networks (SINs) can be linked
to variables like grades [1], persistence [2], and participation
[3]. Recent efforts in the field of network science have been
done to investigate layered - or multiplex - networks as
mathematical objects [4]. These networks can be explored
via centrality measures, which then have to be modified to
suit layered networks. In student interaction networks [1],
a node represents aspects of a student, and links represent
aspects of student interactions. Using longitudinal selfreported
interactions from Danish university students,
this study investigates how target entropy [5,1] and pagerank
[6,7] are affected when we take time and modes of
interaction into account. We present our preliminary
models and results and outline our future work in
this area.