DeepStealth: Game-Based Learning Stealth Assessment With Deep Neural Networks

@article{Min2020DeepStealthGL,
  title={DeepStealth: Game-Based Learning Stealth Assessment With Deep Neural Networks},
  author={Wookhee Min and M. Frankosky and Bradford W. Mott and Jonathan P. Rowe and A. Smith and E. Wiebe and K. Boyer and James C. Lester},
  journal={IEEE Transactions on Learning Technologies},
  year={2020},
  volume={13},
  pages={312-325}
}
A distinctive feature of game-based learning environments is their capacity for enabling stealth assessment. Stealth assessment analyzes a stream of fine-grained student interaction data from a game-based learning environment to dynamically draw inferences about students’ competencies through evidence-centered design. In evidence-centered design, evidence models have been traditionally designed using statistical rules authored by domain experts that are encoded using Bayesian networks. This… Expand
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