  Individual behavioral engagement is an important indicator of active learning
in collaborative settings, encompassing multidimensional behaviors mediated
through various interaction modes. Little existing work has explored the use of
multimodal process data to understand individual behavioral engagement in
face-to-face collaborative learning settings. In this study we bridge this gap,
for the first time, introducing a heterogeneous tripartite network approach to
analyze the interconnections among multimodal process data in collaborative
learning. Students' behavioral engagement strategies are analyzed based on
their interaction patterns with various spatial locations and verbal
communication types using a heterogeneous tripartite network. The multimodal
collaborative learning process data were collected from 15 teams of four
students. We conducted stochastic blockmodeling on a projection of the
heterogeneous tripartite network to cluster students into groups that shared
similar spatial and oral engagement patterns. We found two distinct clusters of
students, whose characteristic behavioural engagement strategies were
identified by extracting interaction patterns that were statistically
significant relative to a multinomial null model. The two identified clusters
also exhibited a statistically significant difference regarding students'
perceived collaboration satisfaction and teacher-assessed team performance
level. This study advances collaboration analytics methodology and provides new
insights into personalized support in collaborative learning.
