Cognitive Load Theory in Multimedia Learning: A Review of the Historical Evolution and a Critique on the Theoretical Framework

پژوهش نامه مبانی تعلیم و تربیت

Article 2, Volume 9, Issue 2 - Serial Number 18, March 2020, Pages 22-49   

Document Type: پژوهشی

DOI: 10.22067/fedu.v9i2.76911

Authors

vahid Salehi^{* 1}; Hosein Moradi Mokhles²; Hasan Gharabaghi³

¹Sayyed Jamaladdin Uni.

²Sayyed Jamaleddin Asadabadi

³Malayer University

Abstract

The primary purpose of this study was to introduce the cognitive load theory in multimedia learning, review its historical evolution, and present a critique of its theoretical framework. In this paper, we articulated the following three primary stages of development in cognitive load theory;   extraneous cognitive load in problem-solving, intrinsic cognitive load and the first additivity hypothesis, and germane cognitive load and the second additivity hypothesis. Besides, we argued that the germane cognitive load does not play the explanatory and predictive role in cognitive load theory as much as the intrinsic and the extraneous load types. Therefore, a dual framework including intrinsic and extraneous cognitive loads can clarify and simplify the framework of this theory and prevent irregular extension of its boundaries, as well as the inadequate development of instruments for measuring triple cognitive loads.

Keywords

cognitive load theory; multimedia learning; theoretical framework; historical evolution

References

References

Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52, 1–26.

Bannert, M. (2002). Managing cognitive load, recent trends in cognitive load theory. Learning and Instruction, 12, 139–146.

Chen, O., Castro-Alonso, J. C., Paas, F., & Sweller, J. (2017). Extending cognitive load theory to incorporate working memory resource depletion: Evidence from the spacing effect. Educational Psychology Review, 1-19.

Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24, 87-114.

Grobe, C. S., & Renkl, A. (2006). Effects of multiple solution methods in mathematics learning. Learning and Instruction, 16, 122–138.

Grobe, C. S., & Renkl, A. (2007). Finding and fixing errors in worked examples: Can this foster learning outcomes? Learning and Instruction, 17, 612–634.

Hancock, P. A., & Desmond, P. A. (2001). Stress, workload and fatigue. London: Lawrence Erlbaum Associates.

Jonassen, D., & Land, S. (Eds.). (2012). Theoretical foundations of learning environments. Routledge.

Kalyuga, S. (2009). Managing Cognitive Load in Adaptive Multimedia Learning. New York: Hershey.

Kalyuga, S., Chandler, P., & Sweller, J. (1998). Levels of expertise and instructional design. Human Factors, 40, 1–17.

Kirshner, P., Sweller, J., & Clark, R. (2006).Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem based, experiential, and inquiry-based teaching. Educational Psychologist, 41, 75–86.

Kulasegaram, K., Axelrod, D., Ringsted, C., & Brydges, R. (2018). Do one then see one: Sequencing discovery learning and direct instruction for simulation-based technical skills training. Academic Medicine, 93(11S), S37-S44.

Moreno, R. (2005). Instructional technology: Promise and pitfalls. In L. PytlikZillig, M. Bodvarsson, & R. Bruning (Eds.), Technology-based education: Bringing researchers and practitioners together (pp. 1–19). Greenwich, CT: Information Age Publishing.

Moreno, R. (2006).When worked examples don’t work: Is cognitive load theory at an impasse? Learning and Instruction, 16, 170–181.

Oksa, A., Kalyuga, S., & Chandler, P. (2010). Expertise reversal effect in using explanatory notes for readers of Shakespearean text. Instructional Science, 38, 217–236.

Paas, F. G. W. C., Renkl, A., & Sweller, J. (2003). Cognitive load theory and instructional design: Recent developments. Educational Psychologist, 38, 1–4.

Paas, F., & van Merri¨enboer, J. J. G. (1993). The efficiency of instructional conditions: An approach to combine mental-effort and performance measures. Human Factors, 35, 737–743.

Paas, F., Renkl, A., & Sweller, J. (2004). Cognitive load theory: Instructional implications of the interaction between information structures and cognitive architecture. Instructional Science, 32, 1–8.

Plass, J. L., Moreno, R., Brunken, R. (2010). Cognitive load theory. New York: Cambridge University Press.

Roussel, S., Joulia, D., Tricot, A., & Sweller, J. (2017). Learning subject content through a foreign language should not ignore human cognitive architecture: A cognitive load theory approach. Learning and Instruction, 52, 69-79.

Salehi, V. (2015). Designing and validating English instructional multimedia model based on the effects of different types of cognitive load on learning and retention of novice and expert learners. Doctoral dissertation. Tehran: Allameh Tabataba’i University. (In Persian)

Scheiter, M., & Gerjets, P. (2007). Making your own order: Order effects in system- and usercontrolled settings for learning and problem solving. In F. Ritter, J. Nerb, E. Lehtinen, & T. O’Shea (Eds.), In order to learn: How the sequence of topics influences learning (pp. 195–212). New York: Oxford University Press.

Schmidt, H.G., Loyens, S.M.M., van Gog, T.,& Paas, F. (2007).Problem based learning is compatible with human cognitive architecture: Commentary on Kirschner, Sweller, and Clark (2006). Educational Psychologist, 42, 91–97.

Sweller, J. Ayres, P. & Kalyuga, S. (2011). Cognitive load Theory. New York, Hershey.

Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12, 257–285.

Sweller, J. (1989). Cognitive technology: Some procedures for facilitating learning and problem solving in mathematics and science. Journal of Educational Psychology, 81, 457–466.

Sweller, J. (1993). Some cognitive processes and their consequences for the organization and presentation of information. Australian Journal of Educational Psychology, 45, 1–8.

Sweller, J. (2004). Instructional design consequences of an analogy between evolution by natural selection and human cognitive architecture. Instructional Science, 32, 9-31.

Sweller, J. (2008). Instructional implications of David Geary’s evolutionary educational psychology. Educational Psychologist, 43, 214–216.

Sweller, J. (2011). Cognitive load theory. In Psychology of learning and motivation (Vol. 55, pp. 37-76). Academic Press.

Sweller, J., & Chandler, P. (1994).Why some material is difficult to learn? Cognition and Instruction, 12, 185–233.

Sweller, J., & Paas, F. (2017). Should self-regulated learning be integrated with cognitive load theory? A commentary. Learning and Instruction, 51, 85-89.

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.

Van Gog, T., Paas, F., & Van Merriënboer, J. J. G. (2006). Effects of process-oriented worked examples on troubleshooting transfer performance. Learning and Instruction, 16, 154–164.

Van Gog, T., Paas, F., & Van Merriënboer, J. J. G. (2008). Process-oriented worked examples: Improving transfer performance through enhanced understanding. Instructional Science, 32, 83–98.

Van Merri¨enboer, J. J. G., & Ayres, P. (2005). Research on cognitive load theory and its design implications for e-learning, educational technology, research and development, Educational Technology Research and Development, 53(3), 5–13.

Van Merriënboer, J. J., & Kirschner, P. A. (2017). Ten steps to complex learning: A systematic approach to four-component instructional design. Routledge.

Wickens, C.D., & Hollands, J. G. (2000). Engineering psychology and human performance. Upper Saddle River, NJ: Prentice-Hall.

Winne, P. H. (2005). Key issues in modeling and applying research on self-regulated learning. Applied Psychology: An International Review, 54, 232–238.

Wirth, J., Künsting, J., & Leutner, D. (2013). The impact of goal specificity and goal type on learning outcome and cognitive load. Computers in Human Behavior, 25, 299–305.

Young, J. Q., Van Merrienboer, J., Durning, S., & Ten Cate, O. (2014). Cognitive load theory: Implications for medical education: AMEE guide no. 86. Medical teacher, 36(5), 371-384.

Yuliana, Y., Tasari, T., & Wijayanti, S. (2017). The Effectiveness of Guided Discovery Learning To Teach Integral Calculus for the Mathematics Students of Mathematics Education Widya Dharma University. Infinity Journal, 6(1), 01-10.