In this paper, we present an unsupervised approach for characteriz-
ing students’ learning behaviors in an open-ended learning environment. We
describe our method for generating metrics that describe a learner’s behaviors
and performance using Coherence Analysis. Then we combine feature selection
with a clustering method to group students by their learning behaviors. We
characterize the primary behaviors of each group and link these behaviors to the
students’ ability to build correct models as well as their learning gains derived
from their pre- and post-test scores. Finally, we discuss how this behavior
characterization may contribute to a framework for adaptive scaffolding of
learning behaviors.