The Faces of Engagement: Automatic Recognition of Student Engagementfrom Facial Expressions

@article{Whitehill2014TheFO,
  title={The Faces of Engagement: Automatic Recognition of Student Engagementfrom Facial Expressions},
  author={Jacob Whitehill and Zewelanji Serpell and Yi-Ching Lin and Aysha Foster and Javier R. Movellan},
  journal={IEEE Transactions on Affective Computing},
  year={2014},
  volume={5},
  pages={86-98}
}
Student engagement is a key concept in contemporary education, where it is valued as a goal in its own right. In this paper we explore approaches for automatic recognition of engagement from students' facial expressions. We studied whether human observers can reliably judge engagement from the face; analyzed the signals observers use to make these judgments; and automated the process using machine learning. We found that human observers reliably agree when discriminating low versus high degrees… CONTINUE READING

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References

Publications referenced by this paper.
SHOWING 1-10 OF 59 REFERENCES

Computer Expression Recognition Toolbox

  • 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition
  • 2008
VIEW 20 EXCERPTS

Intelligent tutoring goes to school in the big city

K. R. Koedinger, J. R. Anderson
  • International Journal of Artificial Intelligence in Education, 8:30–43,
  • 1997
VIEW 10 EXCERPTS
HIGHLY INFLUENTIAL

Baker-Rodrigo observation method protocol 1.0 training manual

J. Ocumpaugh, R. S. Baker, M.M.T. Rodrigo
  • Technical report,
  • 2012
VIEW 1 EXCERPT

Multilayer Architectures for Facial Action Unit Recognition

  • IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
  • 2012
VIEW 1 EXCERPT