Characterizing Students’ Learning Behaviors Using Unsupervised Learning Methods

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10331)


In this paper, we present an unsupervised approach for characterizing 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.


Open-ended learning environments Coherence analysis Learner behaviors Unsupervised learning Feature selection 

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Electrical Engineering and Computer Science, Institute for Software Integrated SystemsVanderbilt UniversityNashvilleUSA

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