Proposed framework to manage cognitive load in computer program learning
AIKED'08 Proceedings of the 7th WSEAS International Conference on Artificial intelligence, knowledge engineering and data bases
WSEAS Transactions on Information Science and Applications
Detecting Intentional Errors Using the Pressures Applied to a Computer Mouse
FAC '09 Proceedings of the 5th International Conference on Foundations of Augmented Cognition. Neuroergonomics and Operational Neuroscience: Held as Part of HCI International 2009
Identifying the Nature of Knowledge Using the Pressures Applied to a Computer Mouse
FAC '09 Proceedings of the 5th International Conference on Foundations of Augmented Cognition. Neuroergonomics and Operational Neuroscience: Held as Part of HCI International 2009
Ad-hoc wireless body area network for augmented cognition sensors
FAC'07 Proceedings of the 3rd international conference on Foundations of augmented cognition
Psycho-physiological measures for assessing cognitive load
Proceedings of the 12th ACM international conference on Ubiquitous computing
Freeform pen-input as evidence of cognitive load and expertise
ICMI '11 Proceedings of the 13th international conference on multimodal interfaces
Multimodal behavior and interaction as indicators of cognitive load
ACM Transactions on Interactive Intelligent Systems (TiiS) - Special issue on highlights of the decade in interactive intelligent systems
Using galvanic skin response for cognitive load measurement in arithmetic and reading tasks
Proceedings of the 24th Australian Computer-Human Interaction Conference
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Assessing the cognitive load of a subject performing a computer task using task performance data is normally available at the end of the task. For assessing cognitive load, physiological data has the advantage of being available in real-time and the potential of assessing the affective components of cognitive load. Described are two new methods of assessing cognitive load from eye tracking and the pressures a subject applies to a computer mouse when subjects perform a math task that involves moving targets. Physiological measures that significantly discriminated task difficulty included eye movement, skin conductivity and one of the pressure signals from the computer mouse. Also, in some cases, these physiological measures can be more sensitive than task performance measures of cognitive load (i.e., incorrect actions) to detect interaction effects with task difficulty. The suite of physiological sensors is shown to be a viable alternative or supplement to task performance measures.