Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-26T06:16:00.748Z Has data issue: false hasContentIssue false

8 - Implications of the Four Component Instructional Design Model for Multimedia Learning

from Part II - Theoretical Foundations

Published online by Cambridge University Press:  19 November 2021

By
Liesbeth Kester  and
Jeroen J. G. van Merriënboer
Edited by
Richard E. Mayer  and
Logan Fiorella
Richard E. Mayer
Affiliation:
University of California, Santa Barbara
Logan Fiorella
Affiliation:
University of Georgia
Get access

Summary

The ongoing scientific and technological developments that impact professional performance require professionals to keep their competencies up-to-date which calls for complex learning. Complex learning involves integrating knowledge, skills, and attitudes; coordinating different constituent skills; and often transferring what is learned in school or training settings to daily life and work settings. It requires memory processes and cognitive learning processes aimed at schema construction and schema automation. In line with the 4C/ID model, we claim that four components are necessary to realize complex learning: (1) learning tasks, (2) supportive information, (3) procedural information, and (4) part-task practice.

Keywords

4C/ID modelcomplex learninglearning taskssupportive informationprocedural knowledgepart-task practice
Type
Chapter
Information
The Cambridge Handbook of Multimedia Learning , pp. 100 - 120
Publisher: Cambridge University Press
Print publication year: 2021

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

Agostinho, S., Tindall-Ford, S., Ginns, P., Howard, S. J., Leahy, W., & Paas, F. (2015). Giving learning a helping hand: Finger tracing of temperature graphs on an iPad. Educational Psychology Review, 27, 427–443.CrossRefGoogle Scholar
Ainsworth, S., & VanLabeke, N. (2004). Multiple forms of dynamic representation. Learning and Instruction, 14, 241–255.Google Scholar
Anderson, J. R. (1982). Acquisition of cognitive skill. Psychological Review, 89, 369–406.Google Scholar
Anderson, J. R. (1993). Rules of the Mind. Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
Anderson, J. R., & Lebiere, C. (1998). The Atomic Components of Thought. Mahwah, NJ: Lawrence Erlbaum.Google Scholar
Aoyagi, Y., Ohnishi, E., Yamamoto, Y., Kado, N., Suzuki, T., Ohnishi, H., Hokimoto, H., & Fukaya, N. (2019). Feedback protocol of “fading knowledge of results” is effective for prolonging motor learning retention. Journal of Physical Therapy Science, 31(8), 687–691.Google Scholar
Ausubel, D. P. (1963). The Psychology of Meaningful Verbal Learning. New York: Grune & Stratton.Google Scholar
Baddeley, A. (2009). Long-term and working memory: How do they interact? In Bäckman, L. and Nyberg, L. (eds.), Memory, Aging and the Brain (pp. 17–33). New York: Psychology Press.Google Scholar
Baddeley, A. (2012). Working memory: Theories, models, and controversies. Annual Review of Psychology, 63, 1–29.Google Scholar
Baddeley, A., & Hitch, G. J. (1974). Working memory. In Bower, G. A. (ed.), The Psychology of Learning and Motivation: Advances in Research and Theory (pp. 47–89). New York: Academic Press.Google Scholar
Beckers, J., Dolmans, D., & van Merriënboer, J. (2016). e-Portfolios enhancing students’ self-directed learning: A systematic review of influencing factors. Australasian Journal of Educational Technology, 32(2), 32–46.Google Scholar
Berthold, K., Eysink, T. H. S., & Renkl, A. (2009). Assisting self-explanation prompts are more effective than open prompts when learning with multiple representations. Instructional Science, 37, 345–363.Google Scholar
Bisra, K., Liu, Q., Nesbit, J. C., Salimi, F., & Winne, P. H. (2018). Inducing self-explanation: A meta-analysis. Educational Psychology Review, 30, 703–725.Google Scholar
Braithwaite, D. W., & Goldstone, R. L. (2015). Effects of variation and prior knowledge on abstract concept learning. Cognition and Instruction, 33(3), 226–256.Google Scholar
Briggs, G. E., & Naylor, J. C. (1962). The relative efficiency of several training methods as a function of transfer task complexity. Journal of Experimental Psychology, 64, 505–512.Google Scholar
Burkolter, D., Kluge, A., Sauer, J., & Ritzmann, S. (2010). Comparative study of three training methods for enhancing process control performance: Emphasis shift training, situation awareness training, and drill and practice. Computers in Human Behavior, 26(5), 976–986.Google Scholar
Carlson, R. A., Khoo, H., & Elliot, R. G. (1990). Component practice and exposure to a problem-solving context. Human Factors, 32, 267–286.Google Scholar
Carlson, R. A., Sullivan, M. A., & Schneider, W. (1989). Component fluency in a problem solving context. Human Factors, 31, 489–502.Google Scholar
Carroll, J. M. (2000). Making Use: Scenario-based Design of Human–Computer Interactions. Cambridge, MA: MIT Press.Google Scholar
Castro-Alonso, J. C., Wong, M., Adesope, O. O., Ayres, P., & Paas, F. (2019). Gender imbalance in instructional dynamic versus static visualizations: A meta-analysis. Educational Psychology Review, 31, 361–387.Google Scholar
Chen, O., Kalyuga, S., & Sweller, J. (2017). The expertise reversal effect is a variant of the more general element interactivity effect. Educational Psychology Review, 29, 393–405.Google Scholar
Chi, M. T., de Leeuw, N., Chiu, M. H., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18(3), 439–477.Google Scholar
Ching, B. H. H., & Wu, X. (2019). Concreteness fading fosters children’s understanding of the inversion concept in addition and subtraction. Learning and Instruction, 61, 148–159.CrossRefGoogle Scholar
Cho, Y. H., & Jonassen, D. H. (2012). Learning by self-explaining causal diagrams in high-school biology. Asia Pacific Education Review, 13(1), 171–184.Google Scholar
Clark, R. E., & Estes, F. (1999). The development of authentic educational technologies. Educational Technology, 39(2), 5–16.Google Scholar
Coppens, L., de Jonge, M., van Gog, T., & Kester, L. (2020). The effect of practice test modality on perceived mental effort and delayed final test performance. Journal of Cognitive Psychology, 32(8), 1–7.Google Scholar
Corbalan, G., Kester, L., & van Merriënboer, J. J. G. (2006). Towards a personalized task selection model with shared instructional control. Instructional Science, 34, 399–422.Google Scholar
Corbalan, G., Kester, L., & van Merriënboer, J. J. G. (2008). Selecting learning tasks: Effects of adaptation and shared control on efficiency and task involvement. Contemporary Educational Psychology, 33, 733–756.Google Scholar
Corbalan, G., Kester, L., & van Merriënboer, J. J. G. (2009). Combining shared control with variability over surface features: Effects on transfer test performance and task involvement. Computers in Human Behavior, 25, 290–298.Google Scholar
Corradi, D., Elen, J., & Clarebout, G. (2012). Understanding and enhancing the use of multiple representations in chemistry education. Journal of Science Education and Technology, 21, 780–795.Google Scholar
Cowan, N. (2008). What are the differences between long-term, short-term, and working memory? Progress in Brain Research, 169, 323–338.Google Scholar
Dankbaar, M. (2017). Serious games and blended learning; effects on performance and motivation in medical education. Perspectives in Medical Education, 6, 58–60.Google Scholar
Darabi, A., Hemphill, J., Nelson, D. W., Boulware, W., & Liang, X. (2010). Mental model progression in learning the electron transport chain: Effects of instructional strategies and cognitive flexibility. Advances in Health Sciences Education, 15(4), 479–489.CrossRefGoogle ScholarPubMed
de Grave, W. S., Schmidt, H. G., & Boshuizen, H. P. A. (2001). Effects of problem-based discussion on studying a subsequent text: A randomized trial among first year medical students. Instructional Science, 29, 33–44.Google Scholar
de Westelinck, K., Valcke, M., de Craene, B., & Kirschner, P. (2005). The cognitive theory of multimedia learning in the social sciences knowledge domain: Limitations of external graphical representations. Computers in Human Behavior, 21, 555–573.CrossRefGoogle Scholar
Elio, R. (1986). Representation of similar well-learned cognitive procedures. Cognitive Science, 10, 41–73.Google Scholar
Eshel, Y., & Kohavi, R. (2003). Perceived classroom control, self-regulated learning strategies, and academic achievement. Educational Psychology, 23(3), 249–260.CrossRefGoogle Scholar
Fulgham, S. M. (2008). The Effects of Varying Levels of Support through Worked Examples on Achievement in Software Application Training [Unpublished doctoral dissertation]. Texas Tech University.Google Scholar
Fyfe, E. R., & Nathan, M. J. (2019). Making “concreteness fading” more concrete as a theory of instruction for promoting transfer. Educational Review, 71(4), 403–422.Google Scholar
Gerjets, P., Scheiter, K., & Catrambone, R. (2004). Designing instructional examples to reduce intrinsic cognitive load: Molar versus modular presentation of solution procedures. Instructional Science, 32, 33–58.CrossRefGoogle Scholar
Ginns, P. (2005). Meta-analysis of the modality effect. Learning and Instruction, 4, 313–331.CrossRefGoogle Scholar
Ginns, P. (2006). Integrating information: A meta-analysis of the spatial contiguity and temporal contiguity effects. Learning and Instruction, 16, 511–525.Google Scholar
Gropper, G. L. (1983). A behavioral approach to instructional prescription. In Reigeluth, C. M. (ed.), Instructional Design Theories and Models (pp. 101–161). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Gulikers, J. T. M., Bastiaens, Th. J., & Martens, R. L. (2005). The surplus value of an authentic learning environment. Computers in Human Behavior, 21, 509–521.Google Scholar
Guo, J. P., Pang, M. F., Yang, L. Y., & Ding, Y. (2012). Learning from comparing multiple examples: On the dilemma of “similar” or “different.” Educational Psychology Review, 24(2), 251–269.Google Scholar
Hassanabadi, H., Robatjazi, E. S., & Savoji, A. P. (2011). Cognitive consequences of segmentation and modality methods in learning from instructional animations. Procedia – Social and Behavioral Sciences, 30, 1481–1487.Google Scholar
Hatsidimitris, G., & Kalyuga, S. (2013). Guided self-management of transient information in animations through pacing and sequencing strategies. Educational Technology Research & Development, 61, 91–105.Google Scholar
Höffler, T. N., & Schwartz, R. (2011). Effects of pacing and cognitive style across dynamic and non-dynamic representations. Computers and Education, 57, 1716–1726.Google Scholar
Holland, J. H., Holyoak, K. J., Nisbett, R. E., & Thagard, P. R. (eds.) (1989). Induction: Processes of Inference, Learning, and Discovery. Cambridge, MA: MIT Press.Google Scholar
Hu, F., Ginns, P., & Bobis, J. (2015). Getting the point: Tracing worked examples enhances learning. Learning and Instruction, 35, 85–93.Google Scholar
Hutchins, S. D., Wickens, C. D., Carolan, T. F., & Cumming, J. M. (2013). The influence of cognitive load on transfer with error prevention training methods: A meta-analysis. Human Factors, 55(4), 854–874.Google Scholar
Imhof, B., Scheiter, K., Edelmann, J., & Gerjets, P. (2012). How temporal and spatial aspects of presenting visualizations affect learning about locomotion patterns. Learning and Instruction, 22, 193–205.Google Scholar
Jarodzka, H., van Gog, T., Dorr, M., Scheiter, K., & Gerjets, P. (2013). Learning to see: Guiding students’ attention via a model’s eye movements fosters learning. Learning and Instruction, 25, 62–70.Google Scholar
Johnson, C. I., & Mayer, R. E. (2010). Adding the self-explanation principle to multimedia learning in a computer-based game-like environment. Computers in Human Behavior, 26, 1246–1252.Google Scholar
Kalyuga, S. (2008). Relative effectiveness of animated and static diagrams: An effect of learner prior knowledge. Computers in Human Behavior, 24, 852–861.Google Scholar
Kant, J. M., Scheiter, K., & Oschatz, K. (2017). How to sequence video modeling examples and inquiry tasks to foster scientific reasoning. Learning and Instruction, 52, 46–58.Google Scholar
Karaoğlan Yılmaz, F. G., Olpak, Y. Z., & Yılmaz, R. (2018). The effect of the metacognitive support via pedagogical agent on self-regulation skills. Journal of Educational Computing Research, 56(2), 159–180.Google Scholar
Khacharem, A., Spanjers, I., Zoudji, B., Kalyuga, S., & Ripoll, H. (2012). Using segmentation to support the learning from animated soccer scenes: An effect of prior knowledge. Psychology of Sport and Exercise, 14, 154–160.Google Scholar
Khacharem, A., Trabelsi, K., Engel, F. A., Sperlich, B., & Kalyuga, S. (2020). The effects of temporal contiguity and expertise on acquisition of tactical movements. Frontiers in Psychology, 11, 413.CrossRefGoogle ScholarPubMed
Kicken, W., Brand-Gruwel, S., van Merriënboer, J. J. G., & Slot, W. (2009a). Design and evaluation of a development portfolio: How to improve students’ self-directed learning skills. Instructional Science, 37, 453–473.Google Scholar
Kicken, W., Brand-Gruwel, S., van Merriënboer, J. J. G., & Slot, W. (2009b). The effects of portfolio-based advice on the development of self-directed learning skills in secondary vocational education. Educational Technology, Research and Development, 57, 439–460.Google Scholar
Kim, J., Park, J. H., & Shin, S. (2016). Effectiveness of simulation-based nursing education depending on fidelity: A meta-analysis. BMC Medical Education, 16(1), 152.Google Scholar
Kirschner, P. A. (2017). Stop propagating the learning styles myth. Computers & Education, 106, 166–171.Google Scholar
Kluge, A., Ritzmann, S., Burkolter, D., & Sauer, J. (2011). The interaction of drill and practice and error training with individual differences. Cognition, Technology & Work, 13(2), 103–120.Google Scholar
Kostons, D., & van der Werf, G. (2015). The effects of activating prior topic and metacognitive knowledge on text comprehension scores. British Journal of Educational Psychology, 85(3), 264–275.Google Scholar
Lee, C. H., & Kalyuga, S. (2011). Effectiveness of on-screen pinyin in learning Chinese: An expertise reversal for multimedia redundancy effect. Computers in Human Behavior, 27, 11–15.Google Scholar
Lee, H. S., Betts, S., & Anderson, J. R. (2015). Not taking the easy road: When similarity hurts learning. Memory & Cognition, 43(6), 939–952.Google Scholar
Lee, J. Y., Donkers, J., Jarodzka, H., Sellenraad, G., & van Merriënboer, J. J. (2020). Different effects of pausing on cognitive load in a medical simulation game. Computers in Human Behavior, 110, 106385.Google Scholar
Leppink, J., Broers, N. J., Imbos, T., van der Vleuten, C. P. M., & Berger, M. P. F. (2012). Self-explanation in the domain of statistics: An expertise reversal effect. Higher Education, 63, 771–785.Google Scholar
Lim, J., Reiser, R., & Olina, Z. (2009). The effects of part-task and whole-task instructional approaches on acquisition and transfer of a complex cognitive skill. Educational Technology Research and Development, 57(1), 61–77.CrossRefGoogle Scholar
Liu, T. C., Lin, Y. C., Hsu, C. Y., Hsu, C. Y., & Paas, F. (2020). Learning from animations and computer simulations: Modality and reverse modality effects. British Journal of Educational Technology, 52(1), 304–317.Google Scholar
Liu, T. C., Lin, Y. C., Tsai, M. J., & Paas, F. (2012). Split-attention and redundancy effects on mobile learning in physical environments. Computers and Education, 56, 172–181.Google Scholar
Long, Y., & Aleven, V. (2017). Enhancing learning outcomes through self-regulated learning support with an Open Learner Model. User Modeling and User-Adapted Interaction, 27(1), 55–88.Google Scholar
Lou, A. J., & Jaeggi, S. M. (2020). Reducing the prior‐knowledge achievement gap by using technology‐assisted guided learning in an undergraduate chemistry course. Journal of Research in Science Teaching, 57(3), 368–392.Google Scholar
Malicka, A. (2018). The role of task sequencing in fluency, accuracy, and complexity: Investigating the SSARC model of pedagogic task sequencing. Language Teaching Research, 24, 642–665.Google Scholar
Marei, H. F., Donkers, J., Al-Eraky, M. M., & van Merrienboer, J. J. (2017). The effectiveness of sequencing virtual patients with lectures in a deductive or inductive learning approach. Medical Teacher, 39(12), 1268–1274.Google Scholar
Marshman, E., deVore, S., & Singh, C. (2020). Holistic framework to help students learn effectively from research-validated self-paced learning tools. Physical Review Physics Education Research, 16(2), 020108.Google Scholar
Mathy, F., Chekaf, M., & Cowan, N. (2018). Simple and complex working memory tasks allow similar benefits of information Compression. Journal of Cognition, 1(1), 31.Google Scholar
Mavroudi, A., Giannakos, M., & Krogstie, J. (2018). Supporting adaptive learning pathways through the use of learning analytics: Developments, challenges and future opportunities. Interactive Learning Environments, 26(2), 206–220.Google Scholar
Mautone, P. D., & Mayer, R. E. (2001). Signaling as a cognitive guide in multimedia learning. Journal of Educational Psychology, 93, 377–389.Google Scholar
Mayer, R. E. (2020). Multimedia Learning (3rd ed.). New York: Cambridge University Press.Google Scholar
Mayer, R. E., & Johnson, C. (2008). Revising the redundancy principle in multimedia learning. Journal of Educational Psychology, 100, 380–386.Google Scholar
Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38, 43–52.Google Scholar
McVee, M. B., Dunsmore, K., & Gavelek, J. R. (2005). Schema theory revisited. Review of Educational Research, 75(4), 531–566.Google Scholar
Means, B., Toyama, Y., Murphy, R., Bakia, M., & Jones, K. (2009). Evaluation of Evidence-Based Practices in Online Learning: A Meta-analysis and Review of Online Learning Studies. Washington, DC: US Department of Education, Office of Planning, Evaluation, and Policy Development.Google Scholar
Means, B., Toyama, Y., Murphy, R., & Bakia, M. (2013). The effectiveness of online and blended learning: A meta-analysis of the empirical literature. Teachers College Record, 115(3), 1–47.Google Scholar
Merrill, M. D. (2002). First principles of instruction. Educational Technology Research and Development, 50(3), 43–59.Google Scholar
Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81.Google Scholar
Moreno, R., & Mayer, R. E. (2002). Verbal redundancy in multimedia learning: When reading helps listening. Journal of Educational Psychology, 94, 156–163.Google Scholar
Morphew, J. W., Gladding, G. E., & Mestre, J. P. (2020). Effect of presentation style and problem-solving attempts on metacognition and learning from solution videos. Physical Review Physics Education Research, 16(1), 010104.Google Scholar
Moussa-Inaty, J., Ayres, P. L., & Sweller, J. (2012). Improving listening skills in English as a foreign language by reading rather than listening: A cognitive load perspective. Applied Cognitive Psychology, 26, 391–402.Google Scholar
Mulder, Y. G., Lazonder, A. W., & de Jong, T. (2011). Comparing two types of model progression in an inquiry learning environment with modelling facilities. Learning and Instruction, 21, 614–624.Google Scholar
Mulder, Y. G., Lazonder, A. W., de Jong, T., Anjewierden, A., & Bollen, L. (2012). Validating and optimizing the effects of model progression in simulation-based inquiry learning. Journal of Science Education and Technology, 21(6), 722–729.Google Scholar
Naylor, J. C. & Briggs, G. E. (1963). Effects of task complexity and task organization on the relative efficiency of part and whole training methods. Journal of Experimental Psychology, 65, 217–224.Google Scholar
Nazari, T., van de Graaf, F. W., Dankbaar, M. E., Lange, J. F., van Merriënboer, J. J., & Wiggers, T. (2020). One step at a time: Step by step versus continuous video-based learning to prepare medical students for performing surgical procedures. Journal of Surgical Education, 77(4), 779–787.Google Scholar
Normadhi, N. B. A., Shuib, L., Nasir, H. N. M., Bimba, A., Idris, N., & Balakrishnan, V. (2019). Identification of personal traits in adaptive learning environment: Systematic literature review. Computers & Education, 130, 168–190.Google Scholar
Nugteren, M. L., Jarodzka, H., Kester, L., & van Merriënboer, J. J. (2020). Guiding secondary school students during task selection. Interactive Learning Environments, 1–15.Google Scholar
Oliveira, A. W., & Brown, A. O. (2016). Exemplification in science instruction: Teaching and learning through examples. Journal of Research in Science Teaching, 53(5), 737–767.Google Scholar
Oliveira, A. W., Johnston, E., & Brown, A. O. (2018). Exemplification in undergraduate biology: Dominant images and their impact on student acquisition of conceptual knowledge. Canadian Journal of Science, Mathematics and Technology Education, 18(4), 313–329.Google Scholar
Ottmar, E., & Landy, D. (2017). Concreteness fading of algebraic instruction: Effects on learning. Journal of the Learning Sciences, 26(1), 51–78.Google Scholar
Paas, F., & van 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.Google Scholar
Palmeri, T. J. (1999). Theories of automaticity and the power law of practice. Journal of Experimental Psychology: Learning, Memory, and Cognition, 25, 543–551.Google Scholar
Penney, C. (1989). Modality effects and the structure of short-term working memory. Memory and Cognition, 17, 398–422.Google Scholar
Perkins, D. N., & Grotzer, T. A. (1997). Teaching intelligence. American Psychologist, 52, 1125–1133.Google Scholar
Perkins, D. N., & Salomon, G. (1989). Are cognitive skills context-bound? Educational Researcher, 18, 16–25.Google Scholar
Plass, J. L., Homer, B. D., & Hayward, E. (2009). Design factors for educationally effective animations and simulations. Journal of Computing in Higher Education, 21, 31–61.Google Scholar
Quilici, J. L., & Mayer, R. E. (1996). Role of examples in how students learn to categorize statistics word problems. Journal of Educational Psychology, 88, 144–161.Google Scholar
Rasch, T., & Schnotz, W. (2009). Interactive and non-interactive pictures in multimedia learning environments: Effects on learning outcomes and learning efficiency. Learning and Instruction, 19, 411–422.Google Scholar
Renkl, A. (1999). Learning mathematics from worked-out examples: Analyzing and fostering self-explanations. European Journal of Psychology of Education, 14, 477–488.Google Scholar
Renkl, A., Atkinson, R. K., & Grosse, C. S. (2004). How fading worked solution steps works – A cognitive load perspective. Instructional Science, 32, 59–82.Google Scholar
Rey, G. D. (2012). A review of research and a meta-analysis of the seductive details effect. Educational Research Review, 7, 216–237.Google Scholar
Ritter, F. E., Tehranchi, F., & Oury, J. D. (2019). ACT‐R: A cognitive architecture for modeling cognition. Wiley Interdisciplinary Reviews: Cognitive Science, 10(3), e1488.Google Scholar
Roelle, J., & Berthold, K. (2013). The expertise reversal effect in prompting focused processing of instructional explanations. Instructional Science, 41(4), 635–656.Google Scholar
Rumelhart, D. E. (1980). Schemata: The building blocks. In Spiro, R. J., Bruce, B. C., and Brewer, W. F. (eds.), Theoretical Issues in Reading Comprehension: Perspectives from Cognitive Psychology, Linguistics, Artificial Intelligence and Education (pp. 33–58). London: Routledge.Google Scholar
Rumelhart, D. E. (1984). Schemata and the cognitive system. In Wyer, R. S. Jr., & Srull, T. K. (eds.), Handbook of Social Cognition (Vol. 1, pp. 161–188). Mahwah, NJ: Lawrence Erlbaum Associates Publishers.Google Scholar
Scheiter, K., Gerjets, P., Huk, T., Imhof, B., & Kammerer, Y. (2009). The effects of realism in learning with dynamic visualizations. Learning and Instruction, 19, 481–494.Google Scholar
Schneider, W., & Detweiler, M. (1988). The role of practice in dual-task performance: Toward workload modeling in a connectionist/-control architecture. Human Factors, 30, 539–566.Google Scholar
Schnotz, W., & Rasch, T. (2005). Enabling, facilitating, and inhibiting effects of animations in multimedia learning: Why reduction of cognitive load can have negative results on learning. Educational Technology, Research and Development, 53, 47–58.Google Scholar
Schroeder, N. L., & Cenkci, A. T. (2018). Spatial contiguity and spatial split-attention effects in multimedia learning environments: A meta-analysis. Educational Psychology Review, 30, 679–701.Google Scholar
Schroeder, N. L., & Cenkci, A. T. (2019). Do measures of cognitive load explain the spatial split-attention principle in multimedia learning environments? A systematic review. Journal of Educational Psychology, 112(2), 254–270.Google Scholar
Seufert, T., Schütze, M., & Brünken, R. (2009). Memory characteristics and modality in multimedia learning: An aptitude-treatment-interaction study. Learning and Instruction, 19, 28–42.Google Scholar
Smith, A., & Ayres, P. (2016). Investigating the modality and redundancy effects for learners with persistent pain. Educational Psychology Review, 28(2), 401–424.Google Scholar
Spanjers, I. A. E., van Gog, T., Wouters, P., & van Merriënboer, J. J. G. (2012). Explaining the segmentation effect in learning from animations: The role of pausing and temporal cueing. Computers and Education, 59, 274–280.Google Scholar
Spanjers, I. A. E., Wouters, P., van Gog, T., & van Merriënboer, J. J. G. (2011). An expertise reversal effect of segmentation in learning from animated worked-out examples. Computers in Human Behavior, 27, 46–52.Google Scholar
Spanjers, I. A., Könings, K. D., Leppink, J., Verstegen, D. M., de Jong, N., Czabanowska, K., & van Merrienboer, J. J. (2015). The promised land of blended learning: Quizzes as a moderator. Educational Research Review, 15, 59–74.Google Scholar
Spector, J. M., & Anderson, T. M. (eds.) (2000). Holistic and Integrated Perspectives on Learning, Technology, and Instruction: Understanding Complexity. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Straetmans, G., Sluijsmans, D. M. A., Bolhuis, B., & van Merriënboer, J. J. G. (2003). Integratie van instructie en assessment in competentiegericht onderwijs [Integration of instruction and assessment in competence based education]. Tijdschrift voor Hoger Onderwijs, 21, 171–197.Google Scholar
Sung, E., & Mayer, R. E. (2012). Affective impact of navigational and signaling aids to e-learning. Computers in Human Behavior, 28, 473–483.Google Scholar
Sweller, J. (2020). Cognitive load theory and educational technology. Educational Technology Research and Development, 68(1), 1–16.Google Scholar
Sweller, J., van Merriënboer, J. J. G., & Paas, F. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10, 251–296.Google Scholar
Sweller, J., van Merriënboer, J. J. G., & Paas, F. (2019). Cognitive architecture and instructional design: 20 years later. Educational Psychology Review, 31, 261–292.Google Scholar
Tabbers, H. K., Martens, R. L., & van Merriënboer, J. J. G. (2004). Multimedia instructions and cognitive load theory: Effects of modality and cueing. British Journal of Educational Psychology, 74(1), 71–82.Google Scholar
Tang, M., Ginns, P., & Jacobson, M. J. (2019). Tracing enhances recall and transfer of knowledge of the water cycle. Educational Psychology Review, 31(2), 439–455.Google Scholar
Tarchi, C. (2015). Fostering reading comprehension of expository texts through the activation of readers’ prior knowledge and inference-making skills. International Journal of Educational Research, 72, 80–88.Google Scholar
van Alten, D. C., Phielix, C., Janssen, J., & Kester, L. (2019). Effects of flipping the classroom on learning outcomes and satisfaction: A meta-analysis. Educational Research Review, 28, 100281.Google Scholar
van Genuchten, E., Scheiter, K., & Schüler, A. (2012). Examining learning from text and pictures for different task types: Does the multimedia effect differ for conceptual, causal, and procedural tasks? Computers in Human Behavior, 28, 2209–2218.Google Scholar
van Gog, T., Jarodzka, H., Scheiter, K., Gerjets, P., & Paas, F. (2009). Attention guidance during example study via the model’s eye movements. Computers in Human Behavior, 25, 785–791.Google Scholar
van Gog, T., & Rummel, N. (2010). Example-based learning: Integrating cognitive and social-cognitive research perspectives. Educational Psychology Review, 22(2), 155–174.Google Scholar
van Merriënboer, J. J. G. (1990a). What Cognitive Science May Learn from Instructional Design: A Case Study in Introductory Computer Programming. Paper Presented at the Annual Meeting of the American Educational Research Association (Boston, MA, April 16–20, 1990).Google Scholar
van Merriënboer, J. J. G. (1990b). Strategies for programming instruction in high school: Program completion vs. program generation. Journal of Educational Computing Research, 6, 265–285.Google Scholar
van Merriënboer, J. J. G. (1997). Training Complex Cognitive Skills. Englewood Cliffs, NJ: Educational Technology Publications.Google Scholar
van Merriënboer, J. J. G., & de Croock, M. B. M. (1992). Strategies for computer-based programming instruction: Program completion vs. program generation. Journal of Educational Computing Research, 8, 365–394.Google Scholar
van Merriënboer, J. J. G., & Kester, L. (2008). Whole task models in education. In Spector, J. M., Merrill, M. D., van Merriënboer, J. J. G., and Driscoll, M. P. (eds.), Handbook of Research on Educational Communications and Technology (pp. 441–456). New York: Routledge.Google Scholar
van Merriënboer, J. J. G., & Kirschner, P. A. (2017). Ten Steps to Complex Learning: A Systematic Approach to Four-Component Instructional Design. New York: Routledge.Google Scholar
van Merriënboer, J. J. G., & van der Vleuten, C. P. (2012). Technology-based assessment in the integrated curriculum. In Mayrath, M. C., Clarke-Midura, J., Robinson, D. H., & Schraw, G. (eds.), Technology-based Assessments for 21st Century Skills (pp. 245–370). Greenwich, CT: Information Age Publishing.Google Scholar
Vandewaetere, M., Desmet, P., & Clarebout, G. (2011). The contribution of learner characteristics in the development of computer-based adaptive learning environments. Computers in Human Behavior, 27(1), 118–130.Google Scholar
Wasson, B., & Kirschner, P. A. (2020). Learning design: European approaches. TechTrends, 64, 1–13.Google Scholar
White, B. Y., & Frederiksen, J. R. (1990). Causal model progressions as a foundation for intelligent learning environments. Artificial intelligence, 42(1), 99–157.Google Scholar
Wickens, C. D., Hutchins, S., Carolan, T., & Cumming, J. (2013). Part task training and increasing difficulty training strategies: A meta-analysis approach. Human Factors: The Journal of the Human Factors and Ergonomics Society, 55, 461–470.Google Scholar
Wickens, C. D., & McCarley, J. S. (2007). Applied Attention Theory. Boca Raton, FL: CRC Press.Google Scholar
Willoughby, T., Wood, E., Desmarais, S., Sims, S., & Kalra, M. (1997). Mechanisms that facilitate the effectiveness of elaboration strategies. Journal of Educational Psychology, 89, 682–685.Google Scholar
Yan, V. X., Soderstrom, N. C., Seneviratna, G. S., Bjork, E. L., & Bjork, R. A. (2017). How should exemplars be sequenced in inductive learning? Empirical evidence versus learners’ opinions. Journal of Experimental Psychology: Applied, 23(4), 403.Google Scholar
Yeh, Y. F., Chen, M. C., Hung, P. H., & Hwang, G. J. (2010). Optimal self-explanation prompt design in dynamic multi-representational learning environments. Computers and Education, 54, 1089–1100.Google Scholar
Zimmerman, B., & Schunk, D. (2001). Self-Regulated Learning and Academic Achievement: Theoretical Perspectives (2nd ed.). Mahwah, NJ: Erlbaum.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] 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
×