Deep Knowledge Tracing

@article{Piech2015DeepKT,
  title={Deep Knowledge Tracing},
  author={Chris Piech and Jonathan Bassen and Jonathan Huang and Surya Ganguli and Mehran Sahami and Leonidas J. Guibas and Jascha Sohl-Dickstein},
  journal={ArXiv},
  year={2015},
  volume={abs/1506.05908}
}
Knowledge tracing---where a machine models the knowledge of a student as they interact with coursework---is a well established problem in computer supported education. Though effectively modeling student knowledge would have high educational impact, the task has many inherent challenges. In this paper we explore the utility of using Recurrent Neural Networks (RNNs) to model student learning. The RNN family of models have important advantages over previous methods in that they do not require the… CONTINUE READING

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Key Quantitative Results

  • A 25% gain in AUC over the best previous result on a knowledge tracing benchmark.

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References

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SHOWING 1-10 OF 33 REFERENCES

Speech recognition with deep recurrent neural networks

  • 2013 IEEE International Conference on Acoustics, Speech and Signal Processing
  • 2013
VIEW 1 EXCERPT