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Automatic Cognitive Load Classification Using High-Frequency Interaction Events: An Exploratory Study

Author

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  • Tao Lin

    (Department of Computer Science, Sichuan University, Chengdu, China)

  • Xiao Li

    (Department of Computer Science, Sichuan University, Chengdu, China)

  • Zhiming Wu

    (Department of Computer Science, Sichuan University, Chengdu, China)

  • Ningjiu Tang

    (Department of Computer Science, Sichuan University, Chengdu, China)

Abstract

There is still a challenge of creating an evaluation method which can not only unobtrusively collect data without supplement equipment but also objectively, quantitatively and in real-time evaluate cognitive load of user based the data. The study explores the possibility of using the features extracted from high-frequency interaction events to evaluate cognitive load to respond to the challenge. Specifically, back-propagation neural networks, along with two feature selection methods (nBset and SFS), were used as the classifier and it was able to use a set of features to differentiate three cognitive load levels with an accuracy of 74.27%. The main contributions of the research are: (1) demonstrating the use of combining machine learning techniques and the HFI features in automatically evaluating cognitive load; (2) showing the potential of using the HFI features in discriminating different cognitive load when suitable classifier and features are adopted.

Suggested Citation

  • Tao Lin & Xiao Li & Zhiming Wu & Ningjiu Tang, 2013. "Automatic Cognitive Load Classification Using High-Frequency Interaction Events: An Exploratory Study," International Journal of Technology and Human Interaction (IJTHI), IGI Global, vol. 9(3), pages 73-88, July.
    • Handle: RePEc:igg:jthi00:v:9:y:2013:i:3:p:73-88
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