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

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

Tao Lin, Xiao Li, Zhiming Wu, Ningjiu Tang
Copyright: © 2013 |Volume: 9 |Issue: 3 |Pages: 16
ISSN: 1548-3908|EISSN: 1548-3916|EISBN13: 9781466634398|DOI: 10.4018/jthi.2013070106
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MLA

Lin, Tao, et al. "Automatic Cognitive Load Classification Using High-Frequency Interaction Events: An Exploratory Study." IJTHI vol.9, no.3 2013: pp.73-88. http://doi.org/10.4018/jthi.2013070106

APA

Lin, T., Li, X., Wu, Z., & Tang, N. (2013). Automatic Cognitive Load Classification Using High-Frequency Interaction Events: An Exploratory Study. International Journal of Technology and Human Interaction (IJTHI), 9(3), 73-88. http://doi.org/10.4018/jthi.2013070106

Chicago

Lin, Tao, et al. "Automatic Cognitive Load Classification Using High-Frequency Interaction Events: An Exploratory Study," International Journal of Technology and Human Interaction (IJTHI) 9, no.3: 73-88. http://doi.org/10.4018/jthi.2013070106

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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.

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