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Evaluating Simplicial Mixtures of Markov Chains for Modeling Student Metacognitive Strategies

  • April GalyardtEmail author
  • Ilya Goldin
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 89)

Abstract

Modeling and discovery of the strategies that students use, both cognitive and metacognitive, is important for building accurate models of student knowledge and learning. We present a simulation study to examine whether simplicial mixtures of Markov chains (SM-MC) can be used to model student metacognitive strategies. We find that SM-MC models cannot be estimated on the moderately sized data sets common in education, and must be adapted to be useful for strategy modeling.

Keywords

Transition Matrice Markov Chain Model Intelligent Tutoring System Metacognitive Strategy Cognitive Tutor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This research has been supported in part by a postdoctoral award from the US Department of Education, Office of Education, Institute of Education Sciences to Ilya Goldin, award #R305B110003.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.University of GeorgiaAthensUSA
  2. 2.Center for Digital DataAnalytics & Adaptive LearningPearsonUSA

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