Skip to main content Accessibility help
×

Temporary Disruption

We have implemented additional assurance measures to protect our systems.

As a result, we have suspended some of our systems and services. We apologise for any temporary disruption some customers may experience.

Hostname: page-component-8678677fb7-q8n7b Total loading time: 0 Render date: 2026-10-06T12:05:09.824Z Has data issue: false hasContentIssue false

9 - Measuring Cognitive Load

Published online by Cambridge University Press:  05 June 2012

By
Roland Brünken ,
Tina Seufert  and
Fred Paas
Edited by
Jan L. Plass ,
Roxana Moreno  and
Roland Brünken
Roland Brünken
Affiliation:
Saarland University
Tina Seufert
Affiliation:
Ulm University
Fred Paas
Affiliation:
Erasmus University Rotterdam
Jan L. Plass
Affiliation:
New York University
Roxana Moreno
Affiliation:
University of New Mexico
Roland Brünken
Affiliation:
Universität des Saarlandes, Saarbrücken, Germany
Get access

Summary

THE PROBLEM OF COGNITIVE LOAD MEASUREMENT: WHAT ARE GOOD COGNITIVE LOAD INDICATORS?

The previous chapters have outlined the basic theoretical assumptions for cognitive load theory (Chapter 2), described how cognitive load affects the process of schema acquisition (Chapter 3), and discussed the role that learners' individual differences play in the process of knowledge construction (Chapter 4). The central problem identified by Cognitive Load Theory (CLT) is that learning is impaired when the total amount of processing requirements exceeds the limited capacity of human working memory.

In addition to the fundamental assumption that learning is a function of available cognitive resources, CLT makes some additional assumptions with respect to the relation among cognitive resources, demands, and learning. The first of these additional assumptions is that instructional design and/or methods may induce either a useful (germane) or a wasteful (extraneous) consumption of cognitive capacity. The second assumption is that the source of cognitive load can also vary depending on the complexity of the task to-be-solved (intrinsic cognitive load defined by element interactivity). There is a large body of empirical research supporting the assumptions of CLT by analyzing the relation between the factors influencing cognitive load and learning outcomes. For example, several empirically well-established instructional design principles (Mayer, 2005) were identified in that line of research, which are discussed in other chapters of this book (see Chapters 7, 8). However, can CLT's assumptions be verified directly?

Information

Type
Chapter
Information
Cognitive Load Theory , pp. 181 - 202
Publisher: Cambridge University Press
Print publication year: 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Book purchase

Temporarily unavailable

References

Aasman, J., Mulder, G., & Mulder, L. J. M. (1987). Operator effort and the measurement of heart-rate variability. Human Factors, 29, 161–170.CrossRefGoogle ScholarPubMed
Ayres, P. (2006). Using subjective measures to detect variations of intrinsic cognitive load within problems. Learning & Instruction, 16, 389–400.CrossRefGoogle Scholar
Beatty, J., & Lucero-Wagoner, B. (2000). The pupillary system. In Cacioppo, J. T., Tassinary, L. G., & Berntson, G. G. (Eds.), Handbook of psychophysiology (2nd ed., pp. 142–162). Cambridge, MA: Cambridge University Press.Google Scholar
Brünken, R., Plass, J. L., & Leutner, D. (2003). Direct measurement of cognitive load in multimedia learning. Educational Psychologist, 38, 53–61.CrossRefGoogle Scholar
Brünken, R., Plass, J. L., & Leutner, D. (2004). Assessment of cognitive load in multimedia learning with dual-task methodology: Auditory load and modality effects. Instructional Science, 32, 115–132.CrossRefGoogle Scholar
Brünken, R., Steinbacher, S., Plass, J. L., & Leutner, D. (2002). Assessment of cognitive load within multimedia learning by the dual task methodology. Experimental Psychology, 49, 109–119.CrossRefGoogle ScholarPubMed
Chandler, P., & Sweller, J. (1996). Cognitive load while learning to use a computer program. Applied Cognitive Psychology, 10, 151–170.3.0.CO;2-U>CrossRefGoogle Scholar
Cronbach, L. J., & Snow, R. E. (1977). Aptitudes and instructional methods. New York: Irvington.Google Scholar
Folker, S., Ritter, H., & Sichelschmidt, L. (2005). Processing and integrating multimodal material. The influence of color-coding. In Bara, B. G., Barsalou, L., & Bucciarelli, M. (Eds.), Proceedings of the 27th annual conference of the Cognitive Science Society 2005 (pp. S. 690–695). July 21–23. Stresa, Italy.Google Scholar
Ginns, P. (2005). Meta-analysis of the modality effect. Learning and Instruction, 15, 313–331.CrossRefGoogle Scholar
Glenberg, A. M., Wilkinson, A. C., & Epstein, W. (1982). The illusion of knowing: Failure in the self-assessment of comprehension. Memory & Cognition, 10, 597–602.CrossRefGoogle Scholar
Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). The expertise reversal effect. Educational Psychologist, 38, 23–31.CrossRefGoogle Scholar
Leutner, D., & Plass, J. L. (1998). Measuring learning styles with questionnaires versus direct observation of preferential choice behavior in authentic learning situations: The visualizer/verbalizer behavior observation scale (vv-bos). Computers in Human Behavior, 14, 543–557.CrossRefGoogle Scholar
Mayer, R. E. (2001). Multimedia learning. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Mayer, R. E. (2005). (Ed.). The Cambridge handbook of multimedia learning. Cambridge UK: Cambridge University Press.CrossRefGoogle Scholar
Mayer, R. E., & Moreno, R. (1998). A split-attention effect in multimedia learning: Evidence for dual processing systems in working memory. Journal of Educational Psychology, 90, 312–320.CrossRefGoogle Scholar
Mayer, R., & Sims, V. (1994). For whom is a picture worth a thousand words? Journal of Educational Psychology, 86, 389–401.CrossRefGoogle Scholar
Möller, J., & Müller-Kalthoff, T. (2000). Lernen mit Hypertext: Effekte von Navigationshilfen und Vorwissen [Learning with hypertext: The impact of navigational aids and prior knowledge]. Zeitschrift für Pädagogische Psychologie [German Journal of Educational Psychology], 14, 116–123.CrossRefGoogle Scholar
Mulder, L. J. M. (1992). Measurement and analysis methods of heart rate and respiration for use in applied environments. Biological Psychology, 34, 205–236.CrossRefGoogle ScholarPubMed
Paas, F., Camp, G., & Rikers, R. (2001). Instructional compensation for age-related cognitive declines: Effects of goal specificity in maze learning. Journal of Educational Psychology, 93, 181–186.CrossRefGoogle Scholar
Paas, F., Tuovinen, J., Tabbers, H., & Gerven, P. W. M. (2003). Cognitive load measurement as a means to advance cognitive load theory. Educational Psychologist, 38, 63–71.CrossRefGoogle Scholar
Paas, F., Tuovinen, J., Merriënboer, J. J. G., & Darabi, A. (2005). A motivational perspective on the relation between mental effort and performance: Optimizing learner involvement in instruction. Educational Technology, Research & Development, 53, 25–33.CrossRefGoogle Scholar
Paas, F., Gerven, P. W. M., & Tabbers, H. K. (2005). The cognitive aging principle in multimedia learning. In Mayer, R. (Ed.), Cambridge handbook of multimedia learning (pp. 339–354). New York: Cambridge University Press.CrossRefGoogle Scholar
Paas, F., & Merriënboer, J. J. G. (1993). The efficiency of instructional conditions: An approach to combine mental-effort and performance measures. Human Factors, 35, 737–743.CrossRefGoogle Scholar
Paas, F., & Merriënboer, J. J. G. (1994). Variability of worked examples and transfer of geometrical problem-solving skills: A cognitive-load approach. Journal of Educational Psychology, 86, 122–133.CrossRefGoogle Scholar
Plass, J. L., Chun, D. M., Mayer, R. E., & Leutner, D. (1998). Supporting visual and verbal learning preferences in a second language multimedia learning environment. Journal of Educational Psychology, 90, 25–35.CrossRefGoogle Scholar
Pollock, E., Chandler, P., & Sweller, J. (2002). Assimilating complex information. Learning and Instruction, 12, 61–86.CrossRefGoogle Scholar
Renkl, A., & Atkinson, R. K. (2003). Structuring the transition from example study to problem solving in cognitive skills acquisition: A cognitive load perspective. Educational Psychologist, 38, 15–22.CrossRefGoogle Scholar
Renkl, A., Gruber, H., Weber, S., Lerche, T., & Schweitzer, K. (2003). Cognitive Load beim Lernen aus Lösungsbeispielen [Cognitive load during learning from worked-out examples]. Zeitschrift für Pädagogische Psychologie [German Journal of Educational Psychology], 17, 93–101.CrossRefGoogle Scholar
Salomon, G. (1984). Computers in education: Setting a research agenda. Educational Technology, 24, 7–11.Google Scholar
Seufert, T., & Brünken, R. (2006). Cognitive load and the format of instructional aids for coherence formation. Applied Cognitive Psychology, 20, 321–331.CrossRefGoogle Scholar
Seufert, T., Jänen, I., & Brünken, R. (2007). The impact of intrinsic cognitive load on the effectiveness of graphical help for coherence formation. Computers in Human Behavior, 23, 1055–1071.CrossRefGoogle Scholar
Tabbers, H. K., Martens, R. L., & Merriënboer, J. J. G. (2004). Multimedia instructions and cognitive load theory: Effects of modality and cueing. British Journal of Educational Psychology, 74, 71–81.CrossRefGoogle ScholarPubMed
Tattersall, A. J., & Hockey, G. R. (1995). Level of operator control and changes in heart rate variability during simulated flight maintenance. Human Factors, 37, 682–698.CrossRefGoogle ScholarPubMed
Tuovinen, J., & Paas, F. (2004). Exploring multidimensional approaches to the efficiency of instructional conditions. Instructional Science, 32, 133–152.CrossRefGoogle Scholar
Gerven, P. W. M., Paas, F., Merriënboer, J. J. G., & Schmidt, H. G. (2004). Memory load and task-evoked pupillary responses in aging. Psychophysiology, 41, 167–175.CrossRefGoogle Scholar
Gerven, P. W. M., Paas, F., Merriënboer, J. J. G., & Schmidt, H. G. (2006). Modality and variability as factors in training the elderly. Applied Cognitive Psychology 20(3), 311–320.CrossRef
Gog, T., & Paas, F. (2008). Instructional efficiency: Revisiting the original construct in educational research. Educational Psychologist, 43, 16–26.Google Scholar
Wallen, E., Plass, J. L., & Brünken, R. (2005). The function of annotations in the comprehension of scientific texts – Cognitive load effects and the impact of verbal ability. Educational Technology, Research & Development, 53, 59–72.CrossRefGoogle Scholar
Whelan, R. R. (2005). The multimedia mind: Measuring cognitive load in multimedia learning. Unpublished doctoral dissertation. New York University, New York.
Whelan, R. R. (2007). Neuroimaging of cognitive load in instructional multimedia. Educational Research Review, 2, 1–12.CrossRefGoogle Scholar
Wickens, C. D. (1984). Processing resources in attention. In Parasuraman, R. & Davies, D. R. (Eds.), Varieties of attention (pp. 63–102). London: Academic Press.Google Scholar
Wild, K., & Schiefele, U. (1994). Lernstrategien im Studium: Ergebnisse zur Faktorenstruktur und Reliabilität eines neuen Fragebogens [Learning strategies during studies: Results on factoral structure and reliability of a new questionnaire]. Zeitschrift für Differentielle und Diagnostische Psychologie [German Journal of Differential and Diagnostical Psychology], 15, 185–200.Google Scholar

Accessibility standard: Unknown

Why this information is here

This section outlines the accessibility features of this content - including support for screen readers, full keyboard navigation and high-contrast display options. This may not be relevant for you.

Accessibility Information

Accessibility compliance for the PDF of this chapter is currently unknown and may be updated in the future.

Save book to Kindle

To save this book to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×