International Conference on User Modeling, Adaptation, and Personalization

UMAP 2015: User Modeling, Adaptation and Personalization pp 216-227 | Cite as

Diagrammatic Student Models: Modeling Student Drawing Performance with Deep Learning

  • Andy Smith
  • Wookhee Min
  • Bradford W. Mott
  • James C. Lester
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9146)

Abstract

Recent years have seen a growing interest in the role that student drawing can play in learning. Because drawing has been shown to contribute to students’ learning and increase their engagement, developing student models to dynamically support drawing holds significant promise. To this end, we introduce diagrammatic student models, which reason about students’ drawing trajectories to generate a series of predictions about their conceptual knowledge based on their evolving sketches. The diagrammatic student modeling framework utilizes deep learning, a family of machine learning methods based on a deep neural network architecture, to reason about sequences of student drawing actions encoded with temporal and topological features. An evaluation of the deep-learning-based diagrammatic student models suggests that it can predict student performance more accurately and earlier than competitive baseline approaches.

Keywords

Student modeling Intelligent tutoring systems Deep learning 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Andy Smith
    • 1
  • Wookhee Min
    • 1
  • Bradford W. Mott
    • 1
  • James C. Lester
    • 1
  1. 1.Center for Educational InformaticsNorth Carolina State UniversityRaleighUSA

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