Skip to main content Accessibility help
×
Home
Hostname: page-component-66d7dfc8f5-g56fq Total loading time: 2.408 Render date: 2023-02-08T18:17:58.686Z Has data issue: true Feature Flags: { "useRatesEcommerce": false } hasContentIssue true

4 - Individual Differences and Cognitive Load Theory

Published online by Cambridge University Press:  05 June 2012

Jan L. Plass
Affiliation:
New York University
Slava Kalyuga
Affiliation:
University of New South Wales
Detlev Leutner
Affiliation:
University of Duisburg-Essen
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 previous chapters discussed sources of cognitive load that are a result of the difficulty of the materials, the design of instruction, and the amount of mental effort invested by learners to process the new information. As outlined in these chapters, the major cause of cognitive load effects is the limited capacity of working memory. In this chapter, we discuss how individual differences relate to the level of cognitive load that a particular learner experiences.

Individual differences in learner characteristics take many different forms, ranging from preferences for learning from different presentation formats (e.g., verbal, pictorial) or modalities (auditory, visual, haptic) and preferences for learning under different environmental conditions (e.g., lighting, noise level, or physical position) to cognitive styles (e.g., field dependency/independency), cognitive abilities (e.g., verbal, spatial ability), and intelligence (Carroll, 1993; Jonassen & Grabowski, 1993). The influence of individual differences on learning has been studied for several decades as aptitude-treatment interactions (ATIs; Cronbach & Snow, 1977; Leutner, 1992; Lohman, 1986; Mayer, Stiehl, & Greeno, 1975; Plass, Chun, Mayer, & Leutner, 1998; Shute, 1992; Snow, 1989, 1994; Snow & Lohman, 1984, 1989). Aptitude-treatment interactions occur when different instructional treatment conditions result in differential learning outcomes depending on student aptitudes, in other words, when the effect of a given treatment is moderated by a given aptitude. Different aptitudes may influence learning in specific instructional environments, and the impact of a particular aptitude on a particular condition may only be observed for a particular type of learning outcome.

Type
Chapter
Information
Cognitive Load Theory , pp. 65 - 88
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.)

References

Anderson, J. R., Corbett, A. T., Fincham, J. M., Hoffman, D., & Pelletier, R. (1992). General principles for an intelligent tutoring architecture. In Shute, V. & Regian, W. (Eds.), Cognitive approaches to automated instruction (pp. 81–106). Hillsdale, NJ: Erlbaum.Google Scholar
Atkinson, R. K., Derry, S. J., Renkl, A., & Wortham, D. W. (2000). Learning from examples: Instructional principles from the worked example research. Review of Educational Research, 70, 181–214.CrossRefGoogle Scholar
Ayres, P. (2005). Impact of reducing intrinsic cognitive load on learning in a mathematical domain. Applied Cognitive Psychology, 20, 287–298.CrossRefGoogle Scholar
Azevedo, R., & Cromley, J. G. (2004). Does training on self-regulated learning facilitate students' learning with hypermedia? Journal of Educational Psychology, 96(3), 523–535.CrossRefGoogle Scholar
Azevedo, R., Cromley, J. G., & Seibert, D. (2004). Does adaptive scaffolding facilitate students' ability to regulate their learning with hypermedia? Contemporary Educational Psychology, 29(3), 344–370.CrossRefGoogle Scholar
Baddeley, A. D. (1986). Working memory. New York: Oxford University Press.Google ScholarPubMed
Bandura, A. (1997). Self-efficacy: The exercise of control. New York: Freeman.Google Scholar
Baumeister, R. F., Heatherton, T. F., & Tice, D. M. (1994). Losing control: How and why people fail at self-regulation. San Diego, CA: Academic Press.Google Scholar
Beishuizen, J. J., & Stoutjesdijk, E. T. (1999). Study strategies in a computer assisted study environment. Learning and Instruction, 9, 281–301.CrossRefGoogle Scholar
Biemiller, A., Shany, M., Inglis, A., & Meichenbaum, D. (1998). Factors influencing children's acquisition and demonstration of self-regulation on academic tasks. In Schunk, D. H., & Zimmerman, B. J. (Eds.), Self-regulated learning: From teaching to self-reflective practice (pp. 203–224). New York: Guilford Publications.Google Scholar
Carroll, J. (1993). Human cognitive abilities. New York: Cambridge University Press.CrossRefGoogle Scholar
Chandler, P., & Sweller, J. (1991). Cognitive load theory and the format of instruction. Cognition and Instruction, 8, 293–332.CrossRefGoogle Scholar
Chang, Y. K., Plass, J. L., & Homer, B. D. (2008). Development and Validation of a Behavioral Measure of Metacognitive Processes (BMMP). Featured Research presentation at the annual convention of the Association for Educational Communication and Technology (AECT) in October, 2008 in Orlando, FL.
Chi, M. T. H., Siler, S., & Jeong, H. (2004). Can tutors monitor students' understanding accurately? Cognition and Instruction, 22, 363–387.CrossRefGoogle Scholar
Cooper, G., & Sweller, J. (1987). Effects of schema acquisition and rule automation on mathematical problem-solving transfer. Journal of Educational Psychology, 79(4), 347–362.CrossRefGoogle Scholar
Cronbach, L. J., & Snow, R. E. (1977). Aptitudes and instructional methods: A handbook for research on interaction. New York: Irvington Publishers.Google Scholar
Bruin, A. B. H., Schmidt, H. G., & Rikers, R. M. J. P. (2005). The role of basic science knowledge and clinical knowledge in diagnostic reasoning: A structural equation modeling approach [Report]. Academic Medicine, 80(8), 765–773.CrossRefGoogle Scholar
Eom, W., & Reister, R. A. (2000). The effects of self-regulation and instructional control on performance and motivation in computer-based instruction. International Journal of Instructional Media, 27(3), 247–260.Google Scholar
Eteläpelto, A. (1993). Metacognition and the expertise of computer program comprehension. Scandinavian Journal of Educational Research, 37(3), 243–254.CrossRefGoogle Scholar
Goldstein, M., Bretan, I., Sallnäs, E.-L., & Björk, H. (1999). Navigational abilities in audial voice-controlled dialogue structures. Behaviour & Information Technology, 18(2), 83–95.CrossRefGoogle Scholar
Graesser, A. C., McNamara, D. S., & VanLehn, K. (2005). Scaffolding deep comprehension strategies through Point&Query, AutoTutor, and iSTART. Educational Psychologist, 40(4), 225–234.CrossRefGoogle Scholar
Griffin, T. (2002). Supporting students with low self-regulation through problem-based learning techniques in online education. Unpublished doctoral dissertation, New York University.Google Scholar
Gyselinck, V., Cornoldi, C., Dubois, V., Beni, R., & Ehrlich, M.-F. (2002). Visuospatial memory and phonological loop in learning from multimedia. Applied Cognitive Psychology, 16, 665–685.CrossRefGoogle Scholar
Hegarty, M., Shah, P., & Miyake, A. (2000). Constraints on using the dual-task methodology to specify the degree of central executive involvement in cognitive tasks. Memory & Cognition, 28(3), 376–385.CrossRefGoogle ScholarPubMed
Hegarty, M., & Waller, D. A. (2005). Individual differences in spatial abilities. In Shah, P., & Miyake, A. (Eds.), The Cambridge handbook of visuospatial thinking (pp. 121–169). New York: Cambridge University Press.CrossRefGoogle Scholar
Hmelo, C., Nagarajan, A., & Day, R. (2000). Effects of high and low prior knowledge on construction of a joint problem space. The Journal of Experimental Education, 69, 36–56.CrossRefGoogle Scholar
Homer, B. D., Plass, J. L., & Blake, L. (2006). The effects of video on cognitive load and social presence in multimedia-learning. Manuscript submitted for publication.Google Scholar
Jonassen, D. H., & Grabowski, B. L. (1993). Handbook of individual differences, learning, and instruction. Hillsdale, NJ: Erlbaum.Google Scholar
Kalyuga, S. (2005). Prior knowledge principle in multimedia learning. In Mayer, R. (Ed.), Cambridge handbook of multimedia learning (pp. 325–337). New York: Cambridge University Press.CrossRefGoogle Scholar
Kalyuga, S. (2006a). Instructing and testing advanced learners: A cognitive load approach. Hauppauge, NY: Nova Science Publishers.Google Scholar
Kalyuga, S. (2006b). Rapid cognitive assessment of learners' knowledge structures. Learning & Instruction, 16, 1–11.CrossRefGoogle Scholar
Kalyuga, S. (2007). Expertise reversal effect and its implications for learner-tailored instruction. Educational Psychology Review, 19, 509–539.CrossRefGoogle Scholar
Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). Expertise reversal effect. Educational Psychologist, 38, 23–31.CrossRefGoogle Scholar
Kalyuga, S., Chandler, P., & Sweller, J. (1998). Levels of expertise and instructional design. Human Factors, 40, 1–17.CrossRefGoogle Scholar
Kalyuga, S., Chandler, P., & Sweller, J. (2000). Incorporating learner experience into the design of multimedia instruction. Journal of Educational Psychology, 92, 126–136.CrossRefGoogle Scholar
Kalyuga, S., Chandler, P., & Sweller, J. (2001). Learner experience and efficiency of instructional guidance, Educational Psychology, 21, 5–23.CrossRefGoogle Scholar
Kalyuga, S., Chandler, P., Tuovinen, J., & Sweller, J. (2001). When problem solving is superior to studying worked examples. Journal of Educational Psychology, 93, 579–588.CrossRefGoogle Scholar
Kalyuga, S., Plass, J. L., Homer, B., Milne, C., & Jordan, T. (2007). Managing cognitive load in computer-based simulations for science education. Paper presented at the UNSW Cognitive Load Theory Conference, 24–26 March 2007 in Sydney, Australia.
Kalyuga, S., & Sweller, J. (2004). Measuring knowledge to optimize cognitive load factors during instruction. Journal of Educational Psychology, 96, 558–568.CrossRefGoogle Scholar
Kalyuga, S., & Sweller, J. (2005). Rapid dynamic assessment of expertise to improve the efficiency of adaptive e-learning. Educational Technology Research and Development, 53, 83–93.CrossRefGoogle Scholar
Kanfer, R., & Ackerman, P. L. (1989). Motivation and cognitive abilities: An integrative aptitude-treatment interaction approach to skill acquisition. Journal of Applied Psychology, 74(4), 657–690.CrossRefGoogle Scholar
Lee, H., Plass, J. L., & Homer, B. D. (2006). Optimizing cognitive load for learning from computer-based science simulations. Journal of Educational Psychology, 89, 902–913.CrossRefGoogle Scholar
Leopold, C., den Elzen-Rump, V., & Leutner, D. (2007). Self-regulated learning from science texts. In Prenzel, M. (Ed.), Studies on the educational quality of schools. The final report on the DFG Priority Programme (pp. 21–53). Münster, Germany: Waxmann.Google Scholar
Leutner, D. (1992). Adaptive Lehrsysteme. Instruktionspsychologische Grundlagen und experimentelle Analysen [Adaptive learning systems, instructional psychology foundations and experimental analyses]. Weinheim, Germany: PVU.Google Scholar
Leutner, D. (2004). Instructional-design principles for adaptivity in open learning environments. In Seel, N. M. & Dijkstra, S. (Eds.), Curriculum, plans, and processes in instructional design: International perspectives (pp. 289–308). Mahwah, NJ: Erlbaum.Google Scholar
Leutner, D., Leopold, C., & den Elzen-Rump, V. (2007). Self-regulated learning with a text-highlighting strategy: A training experiment. Zeitschrift für Psychologie/Journal of Psychology, 215(3), 174–182.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
Lohman, D. F. (1979). Spatial ability: A review and reanalysis of the correlational literature (Stanford University Technical Report No. 8). Stanford, CA: Aptitudes Research Project.Google Scholar
Lohman, D. F. (1986). Predicting mathemathanic effects in the teaching of higher-order thinking skills. Educational Psychologist, 21, 191–208.CrossRefGoogle Scholar
Mayer, R. E. (2001). Multimedia learning. New York: Cambridge University Press.CrossRefGoogle Scholar
Mayer, R., & Gallini, J. (1990). When is an illustration worth ten thousand words? Journal of Educational Psychology, 82, 715–726.CrossRefGoogle Scholar
Mayer, R., Mautone, P., & Prothero, W. (2002). Pictorial aids for learning by doing in a multimedia geology simulation game. Journal of Educational Psychology, 94, 171–185.CrossRefGoogle Scholar
Mayer, R. E., & Sims, V. K. (1994). For whom is a picture worth a thousand words? Extensions of a dual-coding theory of multimedia learning. Journal of Educational Psychology, 86, 389–401.CrossRefGoogle Scholar
Mayer, R., Steinhoff, K., Bower, G., & Mars, R. (1995). A generative theory of textbook design: Using annotated illustrations to foster meaningful learning of science text. Educational Technology Research and Development, 43, 31–43.CrossRefGoogle Scholar
Mayer, R., Stiehl, C., & Greeno, J. (1975). Acquisition of understanding and skill in relation to subjects' preparation and meaningfulness of instruction. Journal of Educational Psychology, 67, 331–350.CrossRefGoogle Scholar
McNamara, D., Kintsch, E., Songer, N. B., & Kintsch, W. (1996). Are good texts always better? Interactions of text coherence, Background knowledge, and levels of understanding in learning from text. Cognition and Instruction, 14, 1–43.CrossRefGoogle Scholar
Miyake, A., & Shah, P. (1999). Models of working memory: Mechanisms of active maintenance and executive control. New York: Cambridge University Press.CrossRefGoogle Scholar
Moreno, R. (2002). Who learns best with multiple representations? Cognitive theory predictions on individual differences in multimedia learning. World Conference on Educational Multimedia, Hypermedia and Telecommunications 2002(1), 1380–1385.Google Scholar
Moreno, R., & Durán, R. (2004). Do multiple representations need explanations? The role of verbal guidance and individual differences in multimedia mathematics learning. Journal of Educational Psychology, 96, 492–503.CrossRefGoogle Scholar
Moreno, R., & Plass, J. L. (2006, April). Individual differences in learning with verbal and visual representations. Paper presented at the Technology and Learning Symposium, New York.Google Scholar
Morgan, M. (1985). Self-monitoring of attained subgoals in private study. Journal of Educational Psychology, 77(6), 623–630.CrossRefGoogle Scholar
Muraven, M., Tice, D. M., & Baumeister, R. F. (1998). Self-control as a limited resource: Regulatory depletion patterns. Journal of Personality and Social Psychology, 74(3), 774–789.CrossRefGoogle ScholarPubMed
Nückles, M., Wittwer, J., & Renkl, A. (2005). Information about a layperson's knowledge supports experts in giving effective and efficient online advice to laypersons. Journal of Experimental Psychology: Applied, 11, 219–236.Google ScholarPubMed
Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2009). Learning Styles: Concepts and Evidence. Psychological Science in the Public Interest, 9(3), 105–119.Google Scholar
Pintrich, P. R., & Groot, E. V. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 82, 33–40.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, 25–36.CrossRefGoogle Scholar
Plass, J. L., Chun, D. M., Mayer, R. E., & Leutner, D. (2003). Cognitive load in reading a foreign language text with multimedia aids and the influence of verbal and spatial abilities. Computers in Human Behavior, 19, 221–243.CrossRefGoogle Scholar
Pollock, E., Chandler, J., & Sweller, J. (2002). Assimilating complex information. Learning and Instruction, 12, 61–86.CrossRefGoogle Scholar
Putnam, R. T. (1987). Structuring and adjusting content for students: A study of live and simulated tutoring of addition. American Educational Research Journal, 24, 13–48.CrossRefGoogle Scholar
Renkl, A. (2005, August). Finding and fixing errors in worked examples: Can this foster learning outcomes? Paper presented at the 11th Biennial Conference of the European Association for Research in Learning and Instruction, Nicosia, Cyprus.
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., Atkinson, R. K., Maier, U. H., & Staley, R. (2002). From example study to problem solving: Smooth transitions help learning. Journal of Experimental Education, 70, 293–315.CrossRefGoogle Scholar
Schoenfeld, A. H. (1987). What's all the fuss about metacognition? In Schoenfeld, A. H. (Ed.), Cognitive science and mathematics education (pp. 189–215). Hillsdale, NJ: Erlbaum.Google Scholar
Shaft, T. M. (1995). Helping programmers understand computer programs: The use of metacognition. Data Base Advances, 26, 25–46.CrossRefGoogle Scholar
Shah, P., & Miyake, A. (1996). The separability of working memory resources for spatial thinking and language processing: An individual differences approach. Journal of Experimental Psychology: General, 125(1), 4–27.CrossRefGoogle ScholarPubMed
Shute, V. J. (1992). Aptitude-treatment interactions and cognitive skill diagnosis. In Regian, J. W. & Shute, V. J. (Eds.), Cognitive approaches to automated instruction (pp. 15–47). Hillsdale, NJ: Erlbaum.Google Scholar
Shute, V., & Towle, B. (2003). Adaptive e-learning. Educational Psychologist, 38, 105–114.CrossRefGoogle Scholar
Snow, R. E. (1989). Aptitude-treatment interaction as a framework for research on individual differences in learning. In Ackerman, P. L., Sternberg, R. J., & Glaser, R. (Eds.), Learning and individual differences. Advances in theory and research (pp. 13–59). New York: Freeman.Google Scholar
Snow, R. (1994). Abilities in academic tasks. In Sternberg, R. & Wagner, R. (Eds.), Mind in context: Interactionist perspectives on human intelligence (pp. 3–37). Cambridge, MA: Cambridge University Press.Google Scholar
Snow, R., & Lohman, D. (1984). Toward a theory of cognitive aptitude for learning from instruction. Journal of Educational Psychology, 76, 347–376.CrossRefGoogle Scholar
Snow, R. E., & Lohman, D. F. (1989). Implications of cognitive psychology for educational measurement. In Linn, R. (Ed.), Educational measurement (pp. 263–331). New York: Macmillan.Google Scholar
Sweller, J. (2005). The redundancy principle in multimedia learning. In Mayer, R. (Ed.), Cambridge handbook of multimedia learning (pp. 159–167). New York: Cambridge.CrossRefGoogle Scholar
Sweller, J., Chandler, P., Tierney, P., & Cooper, M. (1990). Cognitive load and selective attention as factors in the structuring of technical material. Journal of Experimental Psychology: General, 119, 176–192.CrossRefGoogle Scholar
Tarmizi, R., & Sweller, J. (1988). Guidance during mathematical problem solving. Journal of Educational Psychology, 80, 424–436.CrossRefGoogle Scholar
Tuovinen, J., & Sweller, J. (1999). A comparison of cognitive load associated with discovery learning and worked examples. Journal of Educational Psychology, 91, 334–341.CrossRefGoogle Scholar
Merriënboer, J. J. G. (1990). Strategies for programming instruction in high school: Program completion vs. program generation. Journal of Educational Computing Research, 6, 265–287.CrossRefGoogle Scholar
Merriënboer, J. J. G., Kirschner, P. A., & Kester, L. (2003). Taking the load off a learner's mind: Instructional design principles for complex learning. Educational Psychologist, 38, 5–13.CrossRefGoogle Scholar
Vohs, K. D., & Heatherton, T. F. (2000). Self-regulatory failure: A resource-depletion approach. Psychological Science, 11(3), 249–254.CrossRefGoogle ScholarPubMed
Waller, D. (2000). Individual differences in spatial learning from computer-simulated environments. Journal of Experimental Psychology: Applied, 6, 307–321.Google ScholarPubMed
Ward, M., & Sweller, J. (1990). Structuring effective worked examples. Cognition and Instruction, 7, 1–39.CrossRefGoogle Scholar
White, B., & Frederiksen, J. (2005). A theoretical framework and approach for fostering metacognitive development. Educational Psychologist, 40(4), 211–223.CrossRefGoogle Scholar
Winne, P. H. (2001). Self-regulated learning viewed from models of information processing. In Zimmerman, B. J. & Schunk, D. H. (Eds.), Self-regulated learning and academic achievement: Theoretical perspectives (2nd ed., pp. 153–189). Mahwah, NJ: Erlbaum.Google Scholar
Yang, Y. C. (1993). The effects of self-regulatory learning skills and type of instructional control on learning from computer-based instruction. International Journal of Instructional Media, 20, 235–241.Google Scholar
Zimmerman, B. J. (1998). Developing self-fulfilling cycles of academic regulation: An analysis of exemplary instructional models. In Schunk, D. H. & Zimmerman, B. J. (Eds.), Self-regulated learning: From teaching to self-reflective practice (pp. 1–19). New York: Guilford.Google Scholar
Zimmerman, B. J., & Kitsantas, A. (1999). Acquiring writing revision skill: Shifting from process to outcome self-regulatory goals. Journal of Educational Psychology, 91, 241–250.CrossRefGoogle Scholar
Zimmerman, B. J., & Schunk, D. H. (Eds.). (2001). Self-regulated learning and academic achievement: Theoretical perspectives (2nd ed). Mahwah, NJ: Erlbaum.Google Scholar
13
Cited by

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@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
×