Abstract
The main concern of this study is designing a framework in which the instructional material whether visual or aural can be presented in accordance with the cognitive capabilities and limitations of the learner. One of the main issues in the simultaneous application of aural and visual modalities is the amount of the capacity of the working memory of the learners and difference between the speed of integration of verbal and visual modalities in the working memory. Therefore, the existence of a dynamic system that can provide an estimation of the cognitive load of the user in the real-time could help the instructor in the presentation of the material.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Paivio, A.: Mental Representations. Oxford University Press, New York (1990)
Mayer, R.E., Moreno, R.: Nine ways to reduce cognitive load in multimedia learning. Educ. Psychol. 38, 43–52 (2003)
Schnotz, W., Bannert, M.: Construction and interference in learning from multiple representation. Learn. Instr. 13, 141–156 (2003)
Ayres, P., Sweller, J.: The split-attention principle in multimedia learning. In: The Cambridge Handbook of Multimedia Learning, pp. 134–146 (2005)
van Gog, T., Scheiter, K.: Eye tracking as a tool to study and enhance multimedia learning. Learn. Instr. 20, 95–99 (2010)
SanMiguel, I., Corral, M.-J., Escera, C.: When loading working memory reduces distraction: behavioral and electrophysiological evidence from an auditory – visual distraction paradigm. J. Cogn. Neurosci. 20, 1131–1145 (2008)
Chatterjee, D., Sinharay, A., Konar, A.: EEG-based fuzzy cognitive load classification during logical analysis of program segments. In: IEEE International Conference on Fuzzy Systems (2013)
Anderson, E.W., Potter, K.C., Matzen, L.E., Shepherd, J.F., Preston, G.A., Silva, C.T.: A user study of visualization effectiveness using EEG and cognitive load. Comput. Graph. Forum 30, 791–800 (2011)
Kumar, N., Kumar, J.: Measurement of cognitive load in HCI systems using EEG power spectrum: an experimental study. Procedia Comput. Sci. 84, 70–78 (2016)
Antonenko, P., Paas, F., Grabner, R., van Gog, T.: Using electroencephalography to measure cognitive load. Educ. Psychol. Rev. 22, 425–438 (2010)
Tabbers, H.K., Martens, R.L., van Merriënboer, J.J.G.: Multimedia instructions and cognitive load theory: effects of modality and cueing. Br. J. Educ. Psychol. 74, 71–81 (2004)
Chandler, Sweller: J.: The split-attention effect as a factor in the design of instruction. Br. J. Educ. Psychol. 62, 233–246 (1992)
Sweller, J., van Merrienboer, J.J.G., Paas, F.G.W.C.: Cognitive architecture and instructional design. Educ. Psychol. Rev. 10, 251–296 (1998)
Young, J.Q., Van Merrienboer, J., Durning, S., Ten Cate, O.: Cognitive load theory: implications for medical education: AMEE guide no. 86. Med. Teach. 36, 371–384 (2014)
Mayer, R.E.: Applying the science of learning to medical education. Med. Educ. 44, 543–549 (2010)
Lavie, N., Hirst, A., de Fockert, J.W., Viding, E.: Load theory of selective attention and cognitive control. J. Exp. Psychol. Gen. 133, 339–354 (2004)
Kirschner, P.A.: Cognitive load theory: Implications of cognitive load theory on the design of learning. Learn. Instr. 12, 1–10 (2002)
Duchowski, A.: Eye Tracking Methodology: Theory and Practice. Springer, London (2007)
Majooni, A., Masood, M., Akhavan, A.: An eye tracking experiment on strategies to minimize the redundancy and split attention effects in scientific graphs and diagrams. In: Di Bucchianico, G., Kercher, P. (eds.) Advances in Design for Inclusion: Proceedings of the AHFE 2016 International Conference on Design for Inclusion, Walt Disney World®, Florida, USA, 27–31 July 2016, pp. 529–540. Springer International Publishing, Cham (2016)
Van Gerven, P.W.M., Paas, F., Van Merriënboer, J.J.G., Schmidt, H.G.: Memory load and the cognitive pupillary response in aging. Psychophysiology 41, 167–174 (2004)
Kahneman, D., Beatty, J.: Pupil diameter and load on memory. Science 154, 1583–1585 (1966)
Ahern, S., Beatty, J.: Pupillary responses during information processing vary with scholastic aptitude test scores. Science 205, 1289–1292 (1979)
Klingner, J., Kumar, R., Hanrahan, P.: Measuring the task-evoked pupillary response with a remote eye tracker. In: Proceedings of the 2010 Symposium on Eye-Tracking Research and Applications. ACM (2008)
Di Stasi, L.L., Antolí, A., Cañas, J.J.: Evaluating mental workload while interacting with computer-generated artificial environments. Entertain. Comput. 4, 63–69 (2013)
Acknowledgments
This research is supported by University Sains Malaysia, Centre of Instructional Technology and Multimedia (Grant No 203.PMEDIA.6711553).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Majooni, A., Akhavan, A., Masood, M. (2018). Real-Time Cognitive Load Measurement for Dynamic Modality Selection Using Eye-Tracking Methods. In: Di Bucchianico, G., Kercher, P. (eds) Advances in Design for Inclusion. AHFE 2017. Advances in Intelligent Systems and Computing, vol 587. Springer, Cham. https://doi.org/10.1007/978-3-319-60597-5_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-60597-5_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60596-8
Online ISBN: 978-3-319-60597-5
eBook Packages: EngineeringEngineering (R0)