Automatic Cognitive Load Classification Using High-Frequency Interaction Events
| Autor: | Ningjiu Tang, Zhiming Wu, Xiao Li, Tao Lin |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Artificial neural network
business.industry Computer science Exploratory research Pattern recognition Feature selection Machine learning computer.software_genre Human-Computer Interaction Evaluation methods Artificial intelligence business Classifier (UML) computer Cognitive load Information Systems |
| Zdroj: | International Journal of Technology and Human Interaction. 9:73-88 |
| ISSN: | 1548-3916 1548-3908 |
| DOI: | 10.4018/jthi.2013070106 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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