Abstract
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.
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This work has been supported by NSF Cyberlearning Grant #1441542.
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Zhang, N., Biswas, G., Dong, Y. (2017). Characterizing Students’ Learning Behaviors Using Unsupervised Learning Methods. In: André, E., Baker, R., Hu, X., Rodrigo, M., du Boulay, B. (eds) Artificial Intelligence in Education. AIED 2017. Lecture Notes in Computer Science(), vol 10331. Springer, Cham. https://doi.org/10.1007/978-3-319-61425-0_36
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DOI: https://doi.org/10.1007/978-3-319-61425-0_36
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