Understanding Student Success in Chemistry Using Gaze Tracking and Pupillometry

  • Joshua Peterson
  • Zachary Pardos
  • Martina Rau
  • Anna Swigart
  • Colin Gerber
  • Jonathan McKinsey
Conference paper

DOI: 10.1007/978-3-319-19773-9_36

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9112)
Cite this paper as:
Peterson J., Pardos Z., Rau M., Swigart A., Gerber C., McKinsey J. (2015) Understanding Student Success in Chemistry Using Gaze Tracking and Pupillometry. In: Conati C., Heffernan N., Mitrovic A., Verdejo M. (eds) Artificial Intelligence in Education. AIED 2015. Lecture Notes in Computer Science, vol 9112. Springer, Cham

Abstract

Eye tracking allows us to identify visual strategies through gaze behavior, which can help us understand how students process content. Furthermore, understanding which visual strategies are successful can help us improve educational materials that foster successful use of these visual strategies. Previous studies have demonstrated the predictive value of eye tracking for student performance. Chemistry is a highly visual domain, making it particularly appropriate to study visual strategies. Eye tracking also provides measures of pupil dilation that correlate with cognitive processes important to learning, but have not yet been assessed in any realistic learning environments. We examined the gaze behavior and pupil dilation of undergraduate students working with a specialized ITS for chemistry: Chem Tutor. Chem Tutor emphasizes visual learning by focusing specifically on graphical representations. We assessed the value of over 40 high-level gaze features along with measures of pupil diameter to predict student performance and learning gains across an entire chemistry problem set. We found that certain gaze features are strong predictors of performance, but less so of learning gains, while pupil diameter is marginally predictive of learning gains, but not performance. Further studies that assess pupil dilation with higher temporal precision will be necessary to draw conclusions about the limits of its predictive power.

Keywords

Eye tracking Intelligent tutoring systems Performance prediction Chem tutor 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Joshua Peterson
    • 1
  • Zachary Pardos
    • 1
  • Martina Rau
    • 2
  • Anna Swigart
    • 1
  • Colin Gerber
    • 1
  • Jonathan McKinsey
    • 1
  1. 1.University of CaliforniaBerkeleyCalifornia
  2. 2.Department of Educational Psychology, University of WisconsinMadisonWisconsin

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