Modeling Collaborative Discourse with ENA Using a Probabilistic Function

Abstract

Models of collaborative learning need to account for interdependence, the ways in which collaborating individuals construct shared understanding by making connections to one another’s contributions to the collaborative discourse. To operationalize these connections, researchers have proposed two approaches: (1) counting connections based on the presence or absence of events within a temporal window of fixed length, and (2) weighting connections using the probability of one event referring to another. Although most QE researchers use fixed-length windows to model collaborative interdependence, this may result in miscounting connections due to the variability of the appropriate relational context for a given event. To address this issue, we compared epistemic network analysis (ENA) models using both a window function (ENA-W) and a probabilistic function (ENA-P) to model collaborative discourse in an educational simulation of engineering design practice. We conducted a pilot study to compare ENA-W and ENA-P based on (1) interpretive alignment, (2) goodness of fit, and (3) explanatory power, and found that while ENA-P performs slightly better than ENA-W, both ENA-W and ENA-P are feasible approaches for modeling collaborative learning.