Start Date
11-12-2016 12:00 AM
Description
By understanding the psychophysiological factors behind successful e-learning, we aim to identify new techniques that improve participant retention and engagement. Past work has explored the relationship between Electroencephalography (EEG) and learning constructs, such as Cognitive Load and Cognitive Absorption. We believe that the unique application of an e-learning environment warrants an extension of existing theories. Our goal is to develop and validate a model explaining the role of Cognitive Load on Knowledge Gained. This research provides the foundation to then apply this model to create a neuroadaptive learning system. We describe an experiment that uses noninvasive tools to validate this model and explore the viability of off-the-shelf EEG for data collection in e-learning experiments. Potential theoretical contributions are discussed and results from a technical pilot are provided.
Recommended Citation
Conrad, Colin David and Bliemel, Michael, "Psychophysiological Measures of Cognitive Absorption and Cognitive Load in E-Learning Applications" (2016). ICIS 2016 Proceedings. 9.
https://aisel.aisnet.org/icis2016/Human-ComputerInteraction/Presentations/9
Psychophysiological Measures of Cognitive Absorption and Cognitive Load in E-Learning Applications
By understanding the psychophysiological factors behind successful e-learning, we aim to identify new techniques that improve participant retention and engagement. Past work has explored the relationship between Electroencephalography (EEG) and learning constructs, such as Cognitive Load and Cognitive Absorption. We believe that the unique application of an e-learning environment warrants an extension of existing theories. Our goal is to develop and validate a model explaining the role of Cognitive Load on Knowledge Gained. This research provides the foundation to then apply this model to create a neuroadaptive learning system. We describe an experiment that uses noninvasive tools to validate this model and explore the viability of off-the-shelf EEG for data collection in e-learning experiments. Potential theoretical contributions are discussed and results from a technical pilot are provided.