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Predicting Spatial Visualization Problems' Difficulty Level from Eye-Tracking Data.

Publication ,  Journal Article
Li, X; Younes, R; Bairaktarova, D; Guo, Q
Published in: Sensors (Basel, Switzerland)
March 2020

The difficulty level of learning tasks is a concern that often needs to be considered in the teaching process. Teachers usually dynamically adjust the difficulty of exercises according to the prior knowledge and abilities of students to achieve better teaching results. In e-learning, because there is no teacher involvement, it often happens that the difficulty of the tasks is beyond the ability of the students. In attempts to solve this problem, several researchers investigated the problem-solving process by using eye-tracking data. However, although most e-learning exercises use the form of filling in blanks and choosing questions, in previous works, research focused on building cognitive models from eye-tracking data collected from flexible problem forms, which may lead to impractical results. In this paper, we build models to predict the difficulty level of spatial visualization problems from eye-tracking data collected from multiple-choice questions. We use eye-tracking and machine learning to investigate (1) the difference of eye movement among questions from different difficulty levels and (2) the possibility of predicting the difficulty level of problems from eye-tracking data. Our models resulted in an average accuracy of 87.60% on eye-tracking data of questions that the classifier has seen before and an average of 72.87% on questions that the classifier has not yet seen. The results confirmed that eye movement, especially fixation duration, contains essential information on the difficulty of the questions and it is sufficient to build machine-learning-based models to predict difficulty level.

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Published In

Sensors (Basel, Switzerland)

DOI

EISSN

1424-8220

ISSN

1424-8220

Publication Date

March 2020

Volume

20

Issue

7

Start / End Page

E1949

Related Subject Headings

  • Teaching
  • Spatial Navigation
  • Problem Solving
  • Learning
  • Humans
  • Eye-Tracking Technology
  • Eye Movements
  • Analytical Chemistry
  • 4606 Distributed computing and systems software
  • 4104 Environmental management
 

Citation

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Li, X., Younes, R., Bairaktarova, D., & Guo, Q. (2020). Predicting Spatial Visualization Problems' Difficulty Level from Eye-Tracking Data. Sensors (Basel, Switzerland), 20(7), E1949. https://doi.org/10.3390/s20071949
Li, Xiang, Rabih Younes, Diana Bairaktarova, and Qi Guo. “Predicting Spatial Visualization Problems' Difficulty Level from Eye-Tracking Data.Sensors (Basel, Switzerland) 20, no. 7 (March 2020): E1949. https://doi.org/10.3390/s20071949.
Li X, Younes R, Bairaktarova D, Guo Q. Predicting Spatial Visualization Problems' Difficulty Level from Eye-Tracking Data. Sensors (Basel, Switzerland). 2020 Mar;20(7):E1949.
Li, Xiang, et al. “Predicting Spatial Visualization Problems' Difficulty Level from Eye-Tracking Data.Sensors (Basel, Switzerland), vol. 20, no. 7, Mar. 2020, p. E1949. Epmc, doi:10.3390/s20071949.
Li X, Younes R, Bairaktarova D, Guo Q. Predicting Spatial Visualization Problems' Difficulty Level from Eye-Tracking Data. Sensors (Basel, Switzerland). 2020 Mar;20(7):E1949.

Published In

Sensors (Basel, Switzerland)

DOI

EISSN

1424-8220

ISSN

1424-8220

Publication Date

March 2020

Volume

20

Issue

7

Start / End Page

E1949

Related Subject Headings

  • Teaching
  • Spatial Navigation
  • Problem Solving
  • Learning
  • Humans
  • Eye-Tracking Technology
  • Eye Movements
  • Analytical Chemistry
  • 4606 Distributed computing and systems software
  • 4104 Environmental management