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Deep machine learning for Human deep learning

Deep machine learning for Human deep learning
Lee, Doo Seok
DGIST Authors
Lee, Doo Seok
Issue Date
Second IT Revolution, and Dynamic Open Innovation; From Smart City, Autonomous Car, Intelligent Robot, and Block Chain to Sharing Economy
There are many models that represent a student knowledge and content that can be used for personalized instruction, analysis of learning and content analysis. The idea of knowledge representation lies in a high dimensional embedding to capture the dynamics of learning and testing. And knowledge tracing is a kind of sequence model that evaluate the student knowledge variation over time. So we can predict how a student will behave in the future. Recently, the results of using the artificial neural network approach such as RNN (Recurrent Neural Network) and deep reinforcement learning have a significant improvement in the prediction performance of knowledge tracing. In this paper, the author tries to evaluate a student knowledge not in the skills to specific problems but in the the degree of meta-cognitive skills. Using a large-scale data set collected in real-world classrooms, the author adopt the meta leaning machine models to successfully predict student learning outcomes and to improve personalized learning.
SOItmC & Meijo University 2019
Related Researcher
  • Author Lee, Doo Seok  
  • Research Interests CAGD(Computer Aided Geometric Design); Optimization; Smart Education; Machine Learning
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School of Undergraduate Studies2. Conference Papers

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