EEG-Related Changes in Cognitive Workload, Engagement and Distraction as Students Acquire Problem Solving Skills
We have begun to model changes in electroencephalography (EEG)-derived measures of cognitive workload, engagement and distraction as individuals developed and refined their problem solving skills in science. For the same problem solving scenario(s) there were significant differences in the levels and dynamics of these three metrics. As expected, workload increased when students were presented with problem sets of greater difficulty. Less expected, however, was the finding that as skills increased, the levels of workload did not decrease accordingly. When these indices were measured across the navigation, decision, and display events within the simulations significant differences in workload and engagement were often observed. Similarly, event-related differences in these categories across a series of the tasks were also often observed, but were highly variable across individuals.
KeywordsWork Memory Capacity Skill Acquisition Discriminant Function Analysis Main Menu Cognitive Workload
Unable to display preview. Download preview PDF.
- 2.Berka, C., Levendowski, D.J., Cvetinovic, M.M., Davis, G.F., Lumicao, M.N., Popovic, M.V., Zivkovic, V.T., Olmstead, R.E.: Real-Time Analysis of EEG Indices of Alertness, Cognition and Memory Acquired with a Wireless EEG Headset. International Journal of Human-Computer Interaction 17(2), 151–170 (2004)CrossRefGoogle Scholar
- 3.Berka, C., Levendowski, D.J., Ramsey, C.K., Davis, G., Lumicao, M.N., Stanney, K., Reeves, L., Harkness, R.S., Tremoulet, P.D., Stibler, K.: Evaluation of an EEG-Workload Model in an Aegis Simulation, Biomonitoring for Physiological and Cognitive Performance during Military Operations. In: Caldwell, J., Wesentsten, N.J. (eds.) Proceddings of SPIE, vol. 5797, pp. 90–99 (2005)Google Scholar
- 5.Fabiani, M., Gratton, G., Coles, M.G.: Event-related Brain Potentials. In: Cacioppo, J.T., Tassinary, L.G., Berntson, G.G. (eds.) Handbook of Psychophysiology, pp. 53–84. Cambridge University Press, Cambridge (2000)Google Scholar
- 7.Igbal, S.T., Adamczyk, P.D., Zheng, X.S., Bailey, B.P.: Towards an Index of Opportunity: Understanding Changes in Mental Workload During Task Execution. In: Proceedings of the ACM Conference on Human Factors in Computing Systems, Portland, Oregon. USA (2005)Google Scholar
- 8.Lee, J.C., Tan, D.S.: Using a Low-cost Electroencephalogramph for Task Classification in HCI Research. In: UIST 2006. Proceedings of the 19th Annual ACM Symposium on User Interface Software and Technology (Montreux, Switzerland, October 15-18, pp. 81–90. ACM Press, New York, NY (2006)CrossRefGoogle Scholar
- 9.Poythress, M., Russell, C., Siegel, S., Tremoulet, P.D., Craven, P.L., Berka, C., Levendowski, D.J., Chang, D., Baskin, A., Champney, R., Hale, K., Milham, L.: Correlation between Expected Workload and EEG Indices of Cognitive Workload and Task Engagement. In: Proceedings of 2nd Annual Augmented Cognition International Conference, San Francisco, CA (in press)Google Scholar
- 11.Stevens, R., Casillas, A.: Artificial Neural Networks. In: Mislevy, R.E., Williamson, D.M., Bejar, I. (eds.) Automated Scoring, pp. 259–312. Lawrence Erlbaum, Mahwah (2006)Google Scholar
- 12.Stevens, R., Johnson, D.F., Soller, A.: Probabilities and Predictions: Modeling the Development of Scientific Competence. Cell Biology Education, vol. 4(1), pp. 42–57. The American Society for Cell Biology (2005)Google Scholar
- 13.Stevens, R., Soller, A., Cooper, M., Sprang, M.: Modeling the Development of Problem Solving Skills in Chemistry with a Web-Based Tutor. In: Lester, J.C., Vicari, R.M., Paraguaca, F. (eds.) Intelligent Tutoring Systems. 7th International Conference Proceedings, pp. 580–591. Springer-Verlag, Berlin Heidelberg, Germany (2004)Google Scholar
- 14.Stevens, R., Wang, P., Lopo, A.: Artificial Neural Networks can Distinguish Novice and Expert Strategies during Complex Problem Solving. JAMIA 3(2), 131–138 (1996)Google Scholar