Theoretical Foundations

doi:10.1017/9781108894333.007

Part II - Theoretical Foundations

Published online by Cambridge University Press: 19 November 2021

Edited by

Richard E. Mayer and

Logan Fiorella

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Richard E. Mayer: Affiliation:
University of California, Santa Barbara
Logan Fiorella: Affiliation:
University of Georgia

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Type: Chapter
Information: The Cambridge Handbook of Multimedia Learning , pp. 55 - 142

DOI: https://doi.org/10.1017/9781108894333.007 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2021

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References

Azevedo, R., & Aleven, V. (2013). International Handbook of Metacognition and Learning Technologies (Vol. 26). New York: Springer.Google Scholar

Baddeley, A. D. (1999). Human Memory. Boston, MA: Allyn & Bacon.Google Scholar

Baddeley, A. D., Eysenck, M. W., & Anderson, M. C. (2009). Memory. Hove: Psychology Press.Google Scholar PubMed

Chambliss, M. J., & Calfee, R. C. (1998). Textbooks for Learning. Oxford: Blackwell.Google Scholar

Clark, J. M., & Paivio, A. (1991). Dual coding theory and education. Educational Psychology Review, 3(3), 149–210.CrossRef Google Scholar

Conway, A. R. A., & Kovacs, K. (2020). Working memory and intelligence. In Sternberg, R. J. (ed.), The Cambridge Handbook of Intelligence (2nd ed.; pp. 504–527). New York: Cambridge University Press.Google Scholar

Cook, L. K., & Mayer, R. E. (1988). Teaching readers about the structure of scientific text. Journal of Educational Psychology, 80, 448–456.CrossRef Google Scholar

Cowan, N. (2010). The magical mystery four: How is working memory capacity limited and why? Current Directions in Psychological Science, 19(1), 51–57.Google Scholar

Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving students’ learning with effective learning techniques: Promising directions from cognitive and educational psychology. Psychological Science in the Public Interest, 14(1), 4–58.CrossRef Google Scholar PubMed

Fiorella, L., & Mayer, R. E. (2015). Learning as a Generative Activity. New York: Cambridge University Press.CrossRef Google Scholar

Hacker, D. J., Dunlosky, J., & Graesser, A. C. (eds.). (2009). Handbook of Metacognition in Education. New York: Routledge.CrossRef Google Scholar

Huang, X., & Mayer, R. E. (2016). Benefits of adding anxiety-reducing features to a computer-based multimedia lesson. Computers in Human Behavior, 63, 293–303.Google Scholar

Huang, X., & Mayer, R. E. (2019). Adding self-efficacy features to an online statistics lesson. Journal of Educational Computing Research, 57, 1003–1037.Google Scholar

Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Shah, P. (2011). Short-and long-term benefits of cognitive training. Proceedings of the National Academy of Sciences, 108(25), 10081–10086.Google Scholar

Kintsch, W. (1998). Comprehension. New York: Cambridge University Press.Google Scholar

Makransky, G., Wismer, P., & Mayer, R. E. (2019). A gender matching effect in learning with pedagogical agents in an immersive virtual reality science simulation. Journal of Computer Assisted Learning, 35(3), 349–358.Google Scholar

Mayer, R. E. (1989). Systematic thinking fostered by illustrations in scientific text. Journal of Educational Psychology, 81, 240–246.Google Scholar

Mayer, R. E. (1992). Cognition and instruction: Their historic meeting within educational psychology. Journal of Educational Psychology, 84, 405–412.Google Scholar

Mayer, R. E. (1997). Multimedia learning: Are we asking the tight questions? Educational Psychologist, 32, 1–19.Google Scholar

Mayer, R. E. (2001). Multimedia Learning. New York: Cambridge University Press.CrossRef Google Scholar

Mayer, R. E. (2002). Multimedia learning. In Ross, B. H. (ed.), The Psychology of Learning and Motivation: Volume 41 (pp. 85–139). San Diego: Academic Press.Google Scholar

Mayer, R. E. (2003). The promise of multimedia learning: Using the same instructional design methods across different media. Learning and Instruction, 12, 125–141.Google Scholar

Mayer, R. E. (2005). Cognitive theory of multimedia learning. In Mayer, R. E. (ed.), Cambridge Handbook of Multimedia Learning (pp. 31–48). New York: Cambridge University Press.Google Scholar

Mayer, R. E. (2008). Applying the science of learning: Evidence-based principles for the design of multimedia instruction. American Psychologist, 63(8), 760–769.CrossRef Google Scholar PubMed

Mayer, R. E. (2009). Multimedia Learning (2nd ed). New York: Cambridge University Press.Google Scholar

Mayer, R. E. (2011). Applying the Science of Learning. Upper Saddle River, NJ: Pearson.Google Scholar

Mayer, R. E. (2014a). Cognitive theory of multimedia learning. In Mayer, R. E. (ed.), The Cambridge Handbook of Multimedia Learning (2nd ed.; pp. 43–71). New York: Cambridge University Press.Google Scholar

Mayer, R. E. (2014b). Incorporating motivation into multimedia learning. Learning and Instruction, 29, 171–173.Google Scholar

Mayer, R. E. (2019a). Computer games in education. Annual Review of Psychology, 70, 531–549.CrossRef Google Scholar PubMed

Mayer, R. E. (2019b). Cognitive foundations of game-based learning. In Plass, J., Homer, B., & Mayer, R. E. (eds.), Handbook of Game-based Learning (pp. 83–110). Cambridge, MA: MIT Press.Google Scholar

Mayer, R. E. (2021). Multimedia Learning (3rd ed). New York: Cambridge University Press.Google Scholar

Mayer, R. E. (in press). Searching for the role of emotions in e-learning. Learning and Instruction.Google Scholar

Mayer, R. E., & Anderson, R. B. (1991). Animations need narrations: An experimental test of the dual-coding hypothesis. Journal of Educational Psychology, 83, 484–490.CrossRef Google Scholar

Mayer, R. E., & Anderson, R. B. (1992). The instructive animation: Helping students build connections between words and pictures in multimedia learning. Journal of Educational Psychology, 84, 444–452.Google Scholar

Mayer, R. E., Bove, W., Bryman, A., Mars, R., & Tapangco, L. (1996). When less is more: Meaningful learning from visual and verbal summaries of science textbook lessons. Journal of Educational Psychology, 88, 64–73.Google Scholar

Mayer, R. E., & DaPra, S. (2012). An embodiment effect in computer-based learning with animated pedagogical agents. Journal of Experimental Psychology: Applied, 18, 239–252.Google Scholar PubMed

Mayer, R. E., & Estrella, G. (2014). Benefits of emotional design in multimedia instruction. Learning and Instruction, 33, 12–18.CrossRef Google Scholar

Mayer, R. E., & Gallini, J. K. (1990). When is an illustration worth ten thousand words? Journal of Educational Psychology, 82, 715–726.CrossRef Google Scholar

Mayer, R. E., Heiser, J., & Lonn, S. (2001). Cognitive constraints on multimedia learning: When presenting more material results in less understanding. Journal of Educational Psychology, 93, 187–198.Google 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.Google Scholar

Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38, 43–52.Google Scholar

Mayer, R. E., Moreno, R., Boire, M., & Vagge, S. (1999). Maximizing constructivist learning from multimedia communications by minimizing cognitive load. Journal of Educational Psychology, 91, 638–643.Google 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.Google Scholar

Mayer, R. E., 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 & Development, 43, 31–43.Google Scholar

Miller, G. (1956). The magic number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63, 81–97.Google Scholar

Moreno, R. (2007). Optimising learning from animations by minimizing cognitive load: Cognitive and affective consequences of signaling and segmentation methods. Applied Cognitive Psychology, 21, 765–781.Google Scholar

Moreno, R., & Mayer, R. E. (2000). A coherence effect in multimedia learning: The case for minimizing irrelevant sounds in the design of multimedia instructional messages. Journal of Educational Psychology, 92, 117–125.Google Scholar

Moreno, R., & Mayer, R. E. (2007). Interactive multimodal learning environments. Educational Psychology Review, 19, 309–326.CrossRef Google Scholar

National Academies of Sciences, Engineering, and Medicine. (2018). How People Learn II. Washington, DC: National Academies Press.Google Scholar

Paivio, A. (1986). Mental Representations: A Dual Coding Approach. New York: Oxford University Press.Google Scholar

Paivio, A. (2006). Mind and its Evolution: A Dual Coding Approach. Mahwah, NJ: Erlbaum.Google Scholar

Parong, J., Wells, A., & Mayer, R. E. (2020). Replicated evidence towards a cognitive theory of game-based training. Journal of Educational Psychology, 112, 922–937.CrossRef Google 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.Google Scholar

Renninger, K. A., & Hidi, S. E. (2016). The Power of Interest for Motivation and Engagement. New York: Routledge.Google Scholar

Sala, G., & Gobet, F. (2017). Does far transfer exist? Negative evidence from chess, music, and working memory training. Current Directions in Psychological Science, 26(6), 515–520.Google Scholar

Schnotz, W., & Bannert, M. (2003). Construction and interference in learning from multiple representation. Learning and Instruction, 13, 141–156.CrossRef Google Scholar

Schunk, D. H., & DiBenedetto, M. K. (2016). Self-efficacy theory in education. In Wentzel, K. R. & Miele, D. B. (eds.), Handbook of Motivation at School (2nd ed.; pp. 34–54). New York: Routledge.Google Scholar

Sweller, J. (1999). Instructional Design in Technical Areas. Camberwell, Australia: ACER Press.Google Scholar

Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive Load Theory. New York: Springer.Google Scholar

Wittrock, M. C. (1989). Generative processes of comprehension. Educational Psychologist, 24, 345–376.Google Scholar

References

Bandura, A. (1986). Social Foundations of Thought and Action: A Social Cognitive Theory. Englewoods Cliffs, NJ: Prentice Hall.Google Scholar

Chase, W. G., & Simon, H. A. (1973). Perception in chess. Cognitive Psychology, 4, 55–81.Google Scholar

Chen, O., Castro-Alonso, J. C., Paas, F., & Sweller, J. (2018). Extending cognitive load theory to incorporate working memory resource depletion: Evidence from the spacing effect. Educational Psychology Review, 30, 483–501.CrossRef Google Scholar

Chen, O., Kalyuga, S., & Sweller, J. (2015). The worked example effect, the generation effect, and element interactivity. Journal of Educational Psychology, 107, 689–704.Google Scholar

Chen, O., Kalyuga, S., & Sweller, J. (2016a). Relations between the worked example and generation effects on immediate and delayed tests. Learning and Instruction, 45, 20–30.CrossRef Google Scholar

Chen, O., Kalyuga, S., & Sweller, J. (2016b). When instructional guidance is needed. Educational and Developmental Psychologist, 33, 149–162.Google Scholar

De Groot, A. (1965). Thought and Choice in Chess. The Hague, Netherlands: Mouton (Original work published 1946).Google Scholar

Egan, D. E., & Schwartz, B. J. (1979). Chunking in recall of symbolic drawings. Memory and Cognition, 7, 149–158.Google Scholar

Ericsson, K. A., & Kintsch, W. (1995). Long-term working memory. Psychological Review, 102, 211–245.Google Scholar

Geary, D. (2007). Educating the evolved mind: Conceptual foundations for an evolutionary educational psychology. In Carlson, J. S., & Levin, J. R. (eds.), Psychological Perspectives on Contemporary Educational Issues (pp. 1–99). Greenwich, CT: Information Age Publishing.Google Scholar

Geary, D. (2008). An evolutionarily informed education science. Educational Psychologist, 43, 179–195.CrossRef Google Scholar

Geary, D. (2012). Evolutionary educational psychology. In Harris, K., Graham, S., & Urdan, T. (eds.), APA Educational Psychology Handbook (Vol. 1, pp. 597–621). Washington, DC: American Psychological Association.Google Scholar

Geary, D., & Berch, D. (2016). Evolution and children’s cognitive and academic development. In Geary, D., & Berch, D. (eds.), Evolutionary Perspectives on Child Development and Education (pp. 217–249). Switzerland: Springer.CrossRef Google Scholar

Jablonka, E., & Lamb, M. J. (2005). Evolution in Four Dimensions: Genetic, Epigenetic, Behavioral, and Symbolic Variation in the History of Life. Cambridge, MA: MIT Press.Google Scholar

Jeffries, R., Turner, A., Polson, P., & Atwood, M. (1981). Processes involved in designing software. In Anderson, J. R. (ed.), Cognitive Skills and Their Acquisition (pp. 255–283). Hillsdale, NJ: Erlbaum.Google Scholar

Marcus, N., Cooper, M., & Sweller, J. (1996). Understanding instructions. Journal of Educational Psychology, 88, 49–63.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, 81–97.Google Scholar

Newell, A., & Simon, H. A. (1972). Human Problem Solving. Englewood Cliffs, NJ: Prentice Hall.Google Scholar

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

Paas, F., & Sweller, J. (2012). An evolutionary upgrade of cognitive load theory: Using the human motor system and collaboration to support the learning of complex cognitive tasks. Educational Psychology Review, 24, 27–45.Google Scholar

Peterson, L., & Peterson, M. (1959). Short-term retention of individual verbal items. Journal of Experimental Psychology, 58, 193–198.Google Scholar

Simon, H., & Gilmartin, K. (1973). A simulation of memory for chess positions. Cognitive Psychology, 5, 29–46.CrossRef Google Scholar

Sweller, J. (2003). Evolution of human cognitive architecture. In Ross, B. (ed.), The Psychology of Learning and Motivation (Vol. 43, pp. 215–266). San Diego, CA: Academic Press.Google Scholar

Sweller, J. (2010). Element interactivity and intrinsic, extraneous and germane cognitive load. Educational Psychology Review, 22, 123–138.Google Scholar

Sweller, J. (2011). Cognitive load theory. In Mestre, J., & Ross, B. (eds.), The Psychology of Learning and Motivation: Cognition in Education (Vol. 55, pp. 37–76). Oxford: Academic Press.Google Scholar

Sweller, J. (2012). Human cognitive architecture: Why some instructional procedures work and others do not. In Harris, K., Graham, S., & Urdan, T. (eds.), APA Educational Psychology Handbook (Vol. 1, pp. 295–325). Washington, DC: American Psychological Association.Google Scholar

Sweller, J. (2015). In academe, what is learned and how is it learned? Current Directions in Psychological Science, 24, 190–194.CrossRef Google Scholar

Sweller, J. (2016). Working memory, long-term memory and instructional design. Journal of Applied Research in Memory and Cognition, 5, 360–367.Google Scholar

Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive Load Theory. New York: Springer.CrossRef Google Scholar

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

Sweller, J., & Cooper, G. A. (1985). The use of worked examples as a substitute for problem solving in learning algebra. Cognition and Instruction, 2, 59–89.Google Scholar

Sweller, J., & Sweller, S. (2006). Natural information processing systems. Evolutionary Psychology, 4, 434–458.Google Scholar

Sweller, J., van Merriënboer, J., & Paas, F. (2019). Cognitive architecture and instructional design: 20 years later. Educational Psychology Review, 31, 261–292.Google Scholar

Tindall-Ford, S., Chandler, P., & Sweller, J. (1997). When two sensory modes are better than one. Journal of Experimental Psychology: Applied, 3, 257–287.Google Scholar

Tricot, A., & Sweller, J. (2014). Domain-specific knowledge and why teaching generic skills does not work. Educational Psychology Review, 26, 265–283.Google Scholar

van Merriënboer, J., & Sweller, J. (2005). Cognitive load theory and complex learning: Recent developments and future directions. Educational Psychology Review, 17, 147–177.Google Scholar

West-Eberhard, M. (2003). Developmental Plasticity and Evolution. New York: Oxford University Press.Google Scholar

References

Adams, B. C., Bell, L., & Perfetti, C. (1995). A trading relationship between reading skill and domain knowledge in children’s text comprehension. Discourse Processes, 20, 307–323.Google Scholar

Ainsworth, S. (1999). The functions of multiple representations. Computers & Education, 33, 131–152.Google Scholar

Atkinson, C., & Shiffrin, R. M. (1971). The control of short-term memory. Scientific American, 225, 82–90.Google Scholar

Baddeley, A. D. (1986). Working Memory. Oxford: Clarendon Press.Google Scholar

Baddeley, A. D. (1999). Essentials of Human Memory. Hove: Psychology Press.Google Scholar

Baddeley, A. D. (2000). The episodic buffer: A new component of working memory? Trends in Cognitive Science, 4, 417–423.Google Scholar

Caramazza, A., Berndt, R. S., & Basili, A. G. (1983). The selective impairment of phonological processing: A case study. Brain and Language, 18, 128–174.Google Scholar

Carney, R. N., & Levin, J. R. (2002). Pictorial illustrations still improve students’ learning from text. Educational Psychology Review, 14, 5–26.Google Scholar

Chandler, P., & Sweller, J. (1996). Cognitive load while learning to use a computer program. Applied Cognitive Psychology, 10, 151–170.Google Scholar

Coltheart, M., Rastle, K., Perry, C., Langdon, R., & Ziegler, J. (2001). DRC: A dual route cascaded model of visual word recognition and reading aloud. Psychological Review, 108(1), 204–256.Google Scholar

Comenius, J. A. (1999). Orbis sensualium pictus [Facsimile of the 1887 edition]. Whitefish, MT: Kessinger.Google Scholar

Cooney, J. B., & Swanson, H. L. (1991). Learning disabilities and memory. In Wong, B. Y. L. (ed.), Learning about Learning Disabililities (pp. 103–127). Cambridge, MA: Academic Press.Google Scholar

Daneman, M., & Carpenter, P. A. (1983). Individual differences in integrating information between and within sentences. Journal of Experimental Psychology: Learning, Memory and Cognition, 9, 561–583.Google Scholar

Dutke, S. (1996). Generic and generative knowledge: Memory schemata in the construction of mental models. In Battmann, W., & Dutke, S. (eds.), Processes of the Molar Regulation of Behavior (pp. 35–54). Lengerich: Pabst Science.Google Scholar

Eitel, A., & Scheiter, K. (2015). Picture or text first? Explaining sequence effects when learning with pictures and text. Educational Psychology Review, 27, 153–180.Google Scholar

Eitel, A., Scheiter, K., Schüler, A., Nyström, M., & Holmqvist, K. (2013). How a picture facilitates the process of learning from text: Evidence for scaffolding. Learning and Instruction, 28, 48–63.Google Scholar

Ellis, A. W., & Young, A. W. (1996). Human Cognitive Neuropsychology: A Textbook with Readings. Hove: Psychology Press.Google Scholar

Friedman, N. P., & Miyake, A. (2000). Differential roles for visuospatial and verbal working memory in situation model construction. Journal of Experimental Psychology: General, 129, 61–83.Google Scholar

Ginns, P. (2005). Meta-analysis of the modality effect. Learning & Instruction, 15, 313–331.Google Scholar

Graesser, A. C., Millis, K. K., & Zwaan, R. A. (1997). Discourse comprehension. Annual Review of Psychology, 48, 163–189.Google Scholar

Gyselinck, V., Jamet, E., & Dubois, V. (2008). The role of working memory components in multimedia comprehension. Applied Cognitive Psychology, 22, 353–374.CrossRef Google Scholar

Harp, S. F., & Mayer, R. E. (1998). How seductive details do their damage: A theory of cognitive interest in science learning. Journal of Educational Psychology, 90(3), 414–434.Google Scholar

Hochpöchler, U., Schnotz, W., Rasch, T., Ullrich, M., Horz, H., McElvany, N., Schroeder, S., & Baumert, J. (2013). Dynamics of mental model construction from text and graphics. European Journal of Psychology of Education, 28(4), 1105–1126.Google Scholar

Johnson-Laird, P. N. (1983). Mental Models. Cambridge: Cambridge University Press.Google Scholar

Kalyuga, S., Chandler, P., & Sweller, J. (2000). Incorporating learner experience into the design of multimedia instruction. Journal of Educational Psychology, 92, 126–136.Google Scholar

Kintsch, W. (1998). Comprehension: A Paradigm for Cognition. Cambridge: Cambridge University Press.Google Scholar

Kintsch, W., & van Dijk, T. A. (1978). Toward a model of text comprehension and production. Psychological Review, 85, 363–394.Google Scholar

Kirby, J. R., Moore, P. J., & Schofield, N. J. (1988). Verbal and visual learning styles. Contemporary Educational Psychology, 13, 169–184.Google Scholar

Knauff, M., & Johnson-Laird, P. (2002). Visual imagery can impede reasoning. Memory & Cognition, 30, 363–371.Google Scholar

Kosslyn, S. M. (1994). Image and Brain. Cambridge, MA: MIT Press.Google Scholar

Kulhavy, R. W., Stock, W. A., & Caterino, L. C. (1994). Reference maps as a framework for remembering text. In Schnotz, W., & Kulhavy, R. W. (eds.), Comprehension of Graphics (pp. 153–162). Amsterdam: Elsevier Science.Google Scholar

Larkin, J. H., & Simon, H. A. (1987). Why a diagram is (sometimes) worth ten thousand words. Cognitive Science, 11, 65–99.Google Scholar

Leahy, W., Chandler, P., & Sweller, J. (2003). When auditory presentations should and should not be a component of multimedia instruction. Applied Cognitive Psychology, 17, 401–418.Google Scholar

Lenzner, A., Schnotz, W., & Müller, A. (2013). The role of decorative pictures in learning. Instructional Science, 41(5), 811–831.Google Scholar

Levin, J. R., Anglin, G. J., & Carney, R. N. (1987). On empirically validating functions of pictures in prose. In Willows, D. M., & Houghton, H. A. (eds.), The Psychology of Illustration (Vol. 1, pp. 51–86). New York: Springer.Google Scholar

Lindner, M. A., Eitel, A., Strobel, B., & Köller, O. (2017). Identifying processes underlying the multimedia effect in testing: An eye-movement analysis. Learning and Instruction, 47, 91–102.Google Scholar

Lowe, R. K. (1996). Background knowledge and the construction of a situational representation from a diagram. European Journal of Psychology of Education, 11, 377–397.Google Scholar

Marr, D. (1982). Vision. A Computational Investigation into the Human Representation and Processing of Visual Information. San Francisco, CA: Freeman.Google Scholar

Mastropieri, M. A., & Scruggs, T. E. (1989). Constructing more meaningful relationships: Mnemonic instruction for special populations. Educational Psychology Review, 1, 83–111.Google Scholar

Mayer, R. E. (1997). Multimedia learning: Are we asking the right questions? Educational Psychologist, 32, 1–19.Google Scholar

Mayer, R. E. (2009). Multimedia Learning (2d ed.). New York: Cambridge University Press.Google Scholar

Mayer, R. E., & Gallini, J. K. (1990). When is an illustration worth ten thousand words? Journal of Educational Psychology, 82, 715–726.Google Scholar

McNamara, D. S. (ed.) (2007). Reading Comprehension Strategies: Theories, Interventions, and Technologies. New York: Lawrence Erlbaum.CrossRef Google Scholar

McNamara, D. S., 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.Google Scholar

Miller, L. M. S., & Stine-Morrow, E. A. L. (1998). Aging and the effects of knowledge on on-line reading strategies. Journal of Gerontology: Psychology Sciences, 53B, 223–233.Google Scholar

Moreno, R., & Mayer, R. E. (1999). Cognitive principles of multimedia learning: The role of modality and contiguity. Journal of Educational Psychology, 91, 358–368.Google Scholar

Mousavi, S. Y., Low, R., & Sweller, J. (1995). Reducing cognitive load by minimizing auditory and visual presentation modes. Journal of Educational Psychology, 87, 319–334.Google Scholar

Paivio, A. (1986). Mental Representations: A Dual Coding Approach. Oxford: Oxford University Press.Google Scholar

Palmer, S. E., Rosch, E., & Chase, P. (1981). Canonical perspective and the perception of objects. In Long, J., & Baddeley, A. (eds.), Attention and Performance (Vol. 9, pp. 135–151). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar

Peirce, C. S. (1931–1958). Collected Writings, 8 vols. (ed. Hartshorne, C., Weiss, P., & Burks, A. W). Cambridge, MA: Harvard University Press.Google Scholar

Perfetti, C. A., & Britt, M. A. (1995). Where do propositions come from? In Weaver, C. A. III, Mannes, S., & Fletcher, C. R. (eds.), Discourse Comprehension: Essays in Honor of Walter Kintsch (pp. 11–34). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar

Pozzer, L. L., & Roth, W.-M. (2003). Prevalence, function and structure of photographs in high school biology textbooks. Journal of Research in Science Teaching, 40(10), 1089–1114.Google Scholar

Rieben, L., & Perfetti, C. (1991). Learning to Read: Basic Research and its Implications. Hillsdale, NJ: Lawrence Erlbaum.Google Scholar

Rosch, E. (1978). Principles of categorization. In Rosch, E., & Lloyd, B. B. (eds.), Cognition and Categorization (pp. 27–48). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar

Rummer, R., Schweppe, J., Fürstenberg, A., Seufert, T., & Brünken, R. (2010). Working memory interference during processing texts and pictures: Implications for the explanation of the modality effect. Applied Cognitive Psychology, 24, 164–176.Google Scholar

Sanchez, C. A., & Wiley, J. (2006). An examination of the seductive details effect in terms of working memory capacity. Memory & Cognition, 34(2), 344–355.Google Scholar

Schnotz, W. (2011). Colorful bouquets in multimedia research: A closer look at the modality effect. Zeitschrift für Pädagogische Psychologie, 25, 269–276.Google Scholar

Schnotz, W., & Bannert, M. (1999). Einflüsse der Visualisierungsform auf die Konstruktion mentaler Modelle beim Bild- und Textverstehen [Effects of the visualization form on the construction of mental models in picture and text comprehension]. Zeitschrift für Experimentelle Psychologie, 46, 216–235.Google Scholar

Schnotz, W., & Bannert, M. (2003). Construction and interference in learning from multiple representations. Learning and Instruction, 13, 141–156.Google Scholar

Schnotz, W., & Kürschner, C. (2008). External and internal representations in the acquisition and use of knowledge: Visualization effects on mental model construction. Instructional Science, 36, 175–190.Google Scholar

Schnotz, W., & Wagner, I. (2018). Construction and elaboration of mental models through strategic conjoint processing of text and pictures. Journal of Educational Psychology, 110(6), 850–863.Google Scholar

Schüler, A., Scheiter, K., & Schmidt-Weigand, F. (2011). Boundary conditions and constraints of the modality effect. Zeitschrift für Pädagogische Psychologie, 25, 211–220.Google Scholar

Sims, V. K., & Hegarty, M. (1997). Mental animation in the visuospatial sketchpad: Evidence from dual-tasks studies. Memory & Cognition, 25, 321–332.Google Scholar

Soederberg Miller, L. M. (2001). The effects of real-world knowledge on text processing among older adults. Aging, Neuropsychology and Cognition, 8, 137–148.Google Scholar

Stiller, K. D., Freitag, A., Zinnbauer, P., & Freitag, C. (2009). How pacing of multimedia instructions can influence modality effects: A case of superiority of visual texts. Australasian Journal of Educational Technology, 25, 184–203.CrossRef Google Scholar

Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive Load Theory. New York: Springer.Google Scholar

Sweller, J., van Merriënboer, J. G., & Paas, F. G. W. C. (1998). Cognitive architecture and instructional design. Educational Psychological Review, 10, 251–296.Google Scholar

Takahashi, S. (1995). Aesthetic properties of pictorial perception. Psychological Review, 102(4), 671–683.Google Scholar

Vallar, G., & Shallice, T. (eds.) (1990). Neuropsychological Impairments of Short-Term Memory. Cambridge: Cambridge University Press.Google Scholar

van Dijk, T. (1980). Macrostructures: An Interdisciplinary Study of Global Structures in Discourse, Interaction, and Cognition. Hillsdale, NJ: Lawrence Erlbaum.Google Scholar

van Dijk, T. A., & Kintsch, W. (1983). Strategies of Discourse Comprehension. New York: Academic Press.Google Scholar

van Oostendorp, H., & Goldman, S. R. (eds.) (1999). The Construction of Mental Representations during Reading. Hillsdale, NJ: Lawrence Erlbaum.Google Scholar

Weaver, C. A., III, Mannes, S., & Fletcher, C. R. (1995). Discourse Comprehension. Essays in Honor of Walter Kintsch. Hillsdale, NJ: Lawrence Erlbaum.Google Scholar

Zhao, F., Schnotz, W., Wagner, I., & Gaschler, R. (2020). Eyes on text and pictures – Construction and adaptation of mental models. Memory & Cognition, 48(1), 69–82.Google Scholar

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.Google 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.CrossRef 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.CrossRef 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.Google 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.CrossRef 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.Google Scholar

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.Google 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.Google 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.CrossRef Google Scholar

Ginns, P. (2005). Meta-analysis of the modality effect. Learning and Instruction, 4, 313–331.Google Scholar

Ginns, P. (2006). Integrating information: A meta-analysis of the spatial contiguity and temporal contiguity effects. Learning and Instruction, 16, 511–525.CrossRef 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.Google Scholar

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.Google 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

References

Ainley, M. (2006). Connecting with learning: Motivation, affect and cognition in interest processes. Educational Psychology Review, 18(4), 391–405.Google Scholar

Astleitner, H., & Wiesner, C. (2004). An integrated model of multimedia learning and motivation. Journal of Educational Multimedia and Hypermedia, 13(1), 3–21.Google Scholar

Baker, R. S., D’Mello, S. K., Rodrigo, M. M. T., & Graesser, A. C. (2010). Better to be frustrated than bored: The incidence, persistence, and impact of learners’ cognitive–affective states during interactions with three different computer-based learning environments. International Journal of Human–Computer Studies, 68(4), 223–241.Google Scholar

Bandura, A. (1986). Social Foundations of Thought and Action: A Social Cognitive Theory. Englewood Cliffs, NJ: Prentice Hall.Google Scholar

Bandura, A. (1993). Perceived self-efficacy in cognitive development and functioning. Eductional Psychologist, 28, 117–148.Google Scholar

Bandura, A. (2004). Social cognitive theory for personal and social change by enabling media. In Singhal, A., Cody, M. J., Rogers, E. M., & Sabido, M. (eds.), LEA’s Communication Series. Entertainment-Education and Social Change: History, Research, and Practice (pp. 75–96). Mahwah, NJ: Lawrence Erlbaum.Google Scholar

Baylor, A., Ryu, J., & Shen, E. (2003). The effects of pedagogical agent voice and animation on learning, motivation and perceived persona. In Lassner, D., & McNaught, C. (eds.), Proceedings of ED-MEDIA 2003-World Conference on Educational Multimedia, Hypermedia & Telecommunications (pp. 452–458). New York: Association for the Advancement of Computing in Education (AACE).Google Scholar

Bless, H., & Fiedler, K. (2006). Mood and the regulation of information processing and behavior. In Forgas, J. P. (ed.), Affect in Social Thinking and Behavior (pp. 65–84). New York: Psychology Press.Google Scholar

Brom, C., Děchtěrenko, F., Frollová, N., Stárková, T., Bromová, E., & D’Mello, S. K. (2017). Enjoyment or involvement? Affective-motivational mediation during learning from a complex computerized simulation. Computers & Education, 114, 236–254.Google Scholar

Brom, C., Stárková, T., & D’Mello, S. K. (2018). How effective is emotional design? A meta-analysis on facial anthropomorphisms and pleasant colors during multimedia learning. Educational Research Review, 25, 100–119.Google Scholar

Chiu, T. K., Jong, M. S. Y., & Mok, I. A. (2020). Does learner expertise matter when designing emotional multimedia for learners of primary school mathematics? Educational Technology Research and Development, 68(5), 2305–2320.Google Scholar

Clark, R. E. (1983). Reconsidering research on learning from media. Review of Educational Research, 53(4), 445–459.Google Scholar

Clark, R. E. (1994). Media and method. Educational Technology, Research and Development, 42, 7–10.Google Scholar

D’Mello, S. K. (2013). A selective meta-analysis on the relative incidence of discrete affective states during learning with technology. Journal of Educational Psychology, 105, 1082–1099.Google Scholar

D’Mello, S., & Graesser, A. (2007). Monitoring affective trajectories during complex learning. Proceedings of the Annual Meeting of the Cognitive Science Society, 29(29), 203–208.Google Scholar

D’Mello, S., & Graesser, A. (2011). The half-life of cognitive-affective states during complex learning. Cognition & Emotion, 25(7), 1299–1308.Google Scholar

D’Mello, S., & Graesser, A. (2012). Dynamics of affective states during complex learning. Learning and Instruction, 22(2), 145–157.Google Scholar

D’Mello, S., Lehman, B., Pekrun, R., & Graesser, A. (2014). Confusion can be beneficial for learning. Learning and Instruction, 29, 153–170.Google Scholar

Deaney, R., Ruthven, K., & Hennessy, S. (2003). Pupil perspectives on the contribution of information and communication technology to teaching and learning in the secondary school. Research Papers in Education, 18(2), 141–165.Google Scholar

Deci, E. L., & Ryan, R. M. (1985). Conceptualizations of intrinsic motivation and self-determination. In Deci, E. L., & Ryan, R. M. (eds.), Intrinsic Motivation and Self-determination in Human Behavior (pp. 11–40). Boston, MD: Springer.Google Scholar

Deimann, M., & Keller, J. (2006). Volitional aspects of multimedia learning. Journal of Educational Multimedia and Hypermedia, 15(2), 137–158.Google Scholar

Domagk, S., Schwartz, R. N., & Plass, J. L. (2010). Interactivity in multimedia learning: An integrated model. Computers in Human Behavior, 26(5), 1024–1033.Google Scholar

Dweck, C. S., & Elliot, E. S. (1983). Achievement motivation. In Mussen, P. H. (Series ed.) & Hetherington, E. M. (Vol. ed.), Handbook of Child Psychology: Socialization, Personality, and Social Development (4th ed., pp. 643–691). New York: Wiley.Google Scholar

Dweck, C. S., & Leggett, E. L. (1988). A social-cognitive approach to motivation and personality. Psychological Review, 95(2), 256.Google Scholar

Eccles, J. S., Wigfield, A., & Schiefele, U. (1998). Motivation to succeed. In Damon, W., & Eisenberg, N. (eds.). Handbook of Child Psychology (3rd ed., pp. 1017–1095). New York: Wiley.Google Scholar

Ekman, P., Levenson, R. W., & Friesen, W. V. (1983). Autonomic nervous system activity distinguishes among emotions. Science, 221(4616), 1208–1210.Google Scholar

Erhel, S., & Jamet, E. (2013). Digital game-based learning: Impact of instructions and feedback on motivation and learning effectiveness. Computers & Education, 67, 156–167.Google Scholar

Eseryel, D., Law, V., Ifenthaler, D., Ge, X., & Miller, R. (2014). An investigation of the interrelationships between motivation, engagement, and complex problem solving in game-based learning. Educational Technology & Society, 17(1), 42–53.Google Scholar

Faber, J. M., Luyten, H., & Visscher, A. J. (2017). The effects of a digital formative assessment tool on mathematics achievement and student motivation: Results of a randomized experiment. Computers & Education, 106, 83–96.Google Scholar

Feldon, D. F., Callan, G., Juth, S., & Jeong, S. (2019). Cognitive load as motivational cost. Educational Psychology Review, 31, 319–337.Google Scholar

Fraser, K., Huffman, J., Ma, I., Sobczak, M. E., McIlwrick, J., Wright, B., & McLaughlin, K. (2014). The emotional and cognitive impact of unexpected simulated patient death: A randomized controlled trial. Chest, 145(5), 958–963.Google Scholar

Fredrickson, B. L., & Branigan, C. (2005). Positive emotions broaden the scope of attention and thought-action repertoires. Cognition & Emotion, 19(3), 313–332.Google Scholar

Gao, T., & Lehman, J. D. (2003). The effects of different levels of interaction on the achievement and motivation perceptions of college students in a Web-based learning environment. Journal of Interactive Learning Research, 14(4), 367–386.Google Scholar

Gerjets, P., & Scheiter, K. (2003). Goal configurations and processing strategies as moderators between instructional design and cognitive load: Evidence from hypertext-based instruction. Educational Psychologist, 38(1), 33–41.Google Scholar

Graesser, A., Chipman, P., King, B., McDaniel, B., & D’Mello, S. (2007). Emotions and learning with auto tutor. Frontiers in Artificial Intelligence and Applications, 158, 569.Google Scholar

Harp, S. F., & Mayer, R. E. (1997). The role of interest in learning from scientific text and illustrations: On the distinction between emotional interest and cognitive interest. Journal of Educational Psychology, 89(1), 92–102.Google Scholar

Hede, A. (2002). An integrated model of multimedia effects on learning. Journal of Educational Multimedia and Hypermedia, 11, 177–191.Google Scholar

Heidig, S., & Clarebout, G. (2011). Do pedagogical agents make a difference to student motivation and learning? Educational Research Review, 6(1), 27–54.Google Scholar

Heidig, S., Müller, J., & Reichelt, M. (2015). Emotional design in multimedia learning: Differentiation on relevant design features and their effects on emotions and learning. Computers in Human Behavior, 44, 81–95.Google Scholar

Hidi, S., & Renninger, K. A. (2006). The four phase model of interest development. Educational Psychologist, 41(2), 111–127.Google Scholar

Hung, C. M., Huang, I., & Hwang, G. J. (2014). Effects of digital game-based learning on students’ self-efficacy, motivation, anxiety, and achievements in learning mathematics. Journal of Computers in Education, 1(2–3), 151–166.Google Scholar

Hunt, J. M. V. (1965). Intrinsic motivation and its role in psychological development. In Levine, D. (ed.), Nebraska Symposium on Motivation (13th ed., pp. 189–282). Nebraska: University of Nebraska Press.Google Scholar

Isen, A. M., & Reeve, J. (2005). The influence of positive affect on intrinsic and extrinsic motivation: Facilitating enjoyment of play, responsible work behavior, and self-control. Motivation and Emotion, 29(4), 295–323.Google Scholar

Jiang, L., Elen, J., & Clarebout, G. (2009). The relationships between learner variables, tool-usage behaviour and performance. Computers in Human Behavior, 25(2), 501–509.Google Scholar

Keller, J. M. (1987). Motivational design and multimedia: Beyond the novelty effect. Strategic Human Resource Development Review, 1(1), 188–203.Google Scholar

Keller, J. M., & Suzuki, K. (2004). Learner motivation and E-learning design: A multinationally validated process. Journal of Educational Media, 29(3), 229–239.Google Scholar

Knörzer, L., Brünken, R., & Park, B. (2016a). Facilitators or suppressors: Effects of experimentally induced emotions on multimedia learning. Learning and Instruction, 44, 97–107.Google Scholar

Knörzer, L., Brünken, R., & Park, B. (2016b). Emotions and multimedia learning: The moderating role of learner characteristics. Journal of Computer Assisted Learning, 32(6), 618–631.Google Scholar

Korakakis, G., Pavlatou, E. A., Palyvos, J. A., & Spyrellis, N. (2009). 3D visualization types in multimedia applications for science learning: A case study for 8th grade students in Greece. Computers & Education, 52(2), 390–401.Google Scholar

Kort, B., Reilly, R., & Picard, R. W. (2001). An affective model of interplay between emotions and learning: Reengineering educational pedagogy-building a learning companion. In Advanced Learning Technologies. Proceedings. IEEE International Conference (pp. 43–46). IEEE.Google Scholar

Lajoie, S. P., Pekrun, R., Azevedo, R., & Leighton, J. P. (2019). Understanding and measuring emotions in technology-rich learning environments. Learning and Instruction, 70, 101272.Google Scholar

Leutner, D. (2014). Motivation and emotion as mediators in multimedia learning. Learning and Instruction, 29, 174–175.Google Scholar

Li, J., Antonenko, P. D., & Wang, J. (2019). Trends and issues in multimedia learning research in 1996–2016: A bibliometric analysis. Educational Research Review, 28, 1–21.Google Scholar

Liew, T. W., & Tan, S. M. (2016). The effects of positive and negative mood on cognition and motivation in multimedia learning environment. Journal of Educational Technology & Society, 19(2), 104–115.Google Scholar

Liew, T. W., Zin, N. A. M., & Sahari, N. (2017). Exploring the affective, motivational and cognitive effects of pedagogical agent enthusiasm in a multimedia learning environment. Human-Centric Computing and Information Sciences, 7(9), 1–21.Google Scholar

Lin, M. H., Chen, H. C., & Liu, K. S. (2017). A study of the effects of digital learning on learning motivation and learning outcome. Eurasia Journal of Mathematics, Science and Technology Education, 13(7), 3553–3564.Google Scholar

Linnenbrink, E. A., & Pintrich, P. R. (2002). Achievement goal theory and affect: An asymmetrical bidirectional model. Educational Psychologist, 37(2), 69–78.Google Scholar

Loderer, K., Pekrun, R., & Lester, J. C. (2018). Beyond cold technology: A systematic review and meta-analysis on emotions in technology-based learning environments. Learning and Instruction, 70, 101162.Google Scholar

Low, R., & Jin, P. (2009). Motivation and multimedia learning. In Zheng, R. Z. (ed.), Cognitive Effects of Multimedia Learning (pp. 154–172). Hershey, PA: Information Science Reference/IGI Global.Google Scholar

Magner, U. I., Schwonke, R., Aleven, V., Popescu, O., & Renkl, A. (2014). Triggering situational interest by decorative illustrations both fosters and hinders learning in computer-based learning environments. Learning and instruction, 29, 141–152.Google Scholar

Malone, T., & Lepper, M. (1987). Making learning fun: A taxonomy of intrinsic motivations of learning. In Snow, R. E., & Farr, M. J. (eds.), Aptitude, Learning, and Instruction: Conative and Affective ProcessA (pp. 223–253). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar

Meinhardt, J., & Pekrun, R. (2003). Attentional resource allocation to emotional events: An ERP study. Cognition and Emotion, 17(3), 477–500.Google Scholar

Merchant, Z., Goetz, E. T., Keeney-Kennicutt, W., Kwok, O. M., Cifuentes, L., & Davis, T. J. (2012). The learner characteristics, features of desktop 3D virtual reality environments, and college chemistry instruction: A structural equation modeling analysis. Computers & Education, 59(2), 551–568.Google Scholar

Mills, C., D’Mello, S. K., & Kopp, K. (2015). The influence of consequence value and text difficulty on affect, attention, and learning while reading instructional texts. Learning and Instruction, 40, 9–20.Google Scholar

Moos, D. C., & Marroquin, E. (2010). Multimedia, hypermedia, and hypertext: Motivation considered and reconsidered. Computers in Human Behavior, 26(3), 265–276.Google Scholar

Moreno, R. (2006). Does the modality principle hold for different media? A test of the method-affects-learning hypothesis. Journal of Computer Assisted Learning, 22, 149–158.Google Scholar

Moreno, R., & Mayer, R. (2007). Interactive multimodal learning environments. Educational Psychology Review, 19(3), 309–326.Google Scholar

Murphy, K. P., & Alexander, P. A. (2000). A motivated exploration of motivation terminology. Contemporary Educational Psychology, 25, 3–53.Google Scholar

Park, B., Knörzer, L., Plass, J. L., & Brünken, R. (2015). Emotional design and positive emotions in multimedia learning: An eyetracking study on the use of anthropomorphisms. Computers & Education, 86, 30–42.Google Scholar

Park, B., Plass, J. L., & Brünken, R. (2014). Cognitive and affective processes in multimedia learning. Learning and Instruction, 29, 125–127.Google Scholar

Park, S., & Jung, L. (2007). Promoting positive emotion in multimedia learning using visual illustrations. Journal of Educational Multimedia and Hypermedia, 16(2), 141–162.Google Scholar

Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18, 315–341.Google Scholar

Pekrun, R., & Perry, R. P. (2014). Control-value theory of achievement emotions. In Pekrun, R. & Linnenbrink-Garcia, L. (Eds.), International handbook of emotions in education (pp. 120–141). New York, NY: Routledge.Google Scholar

Pintrich, P. R., & Schunk, D. H. (2002). Motivation in Education: Theory, Research and Applications (2nd ed.). Essex: Pearson Prentice Hall.Google Scholar

Plass, J. L., & Kalyuga, S. (2019). Four ways of considering emotion in cognitive load theory. Educational Psychology Review, 31(2), 339–359.Google Scholar

Plass, J. L., & Kaplan, U. (2016). Emotional design in digital media for learning. In Tettegah, S. Y., & Gartmeier, M. (eds.), Emotions, Technology, Design, and Learning (pp. 131–161). London: Elsevier Academic Press.Google Scholar

Plass, J. L., Mayer, R. E., & Homer, B. D. (eds.) (2020). Handbook of Game-Based Learning. Cambridge, MA: MIT Press.Google Scholar

Plass, J. L., & Pawar, S. (2020). Toward a taxonomy of adaptivity for learning. Journal of Research on Technology in Education, 52(3), 275–300.Google Scholar

Roseman, I. J. (1984). Cognitive determinants of emotion: A structural theory. Review of Personality & Social Psychology, 5, 11–36.Google Scholar

Roseman, I. J. (2011). Emotional behaviors, motivational goals, emotion strategies: Multiple levels of organization integrate variable and consistent responses. Emotion Review, 3(4), 434–443.Google Scholar

Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39(6), 1161–1178.Google Scholar

Russell, J. A. (2003). Core affect and the psychological construction of emotion. Psychological Review, 110(1), 145–172.Google Scholar

Ryan, R. M., & Deci, E. L. (2002). Overview of self-determination theory: An organismic-dialectical perspective. In Deci, E. L., & Ryan, R. M. (eds.), Handbook of Self-determination Research (pp. 3–33). Rochester, NY: University of Rochester Press.Google Scholar

Salmerón, L., Kintsch, W., & Cañas, J. J. (2006b). Reading strategies and prior knowledge in learning from hypertext. Memory & Cognition, 34(5), 1157–1171.Google Scholar

Scherer, K. R. (2009). The dynamic architecture of emotion: Evidence for the component process model. Cognition and Emotion, 23(7), 1307–1351.Google Scholar

Scherer, K. R., Shuman, V., Fontaine, J. J. R., & Soriano, C. (2013). The GRID meets the wheel: Assessing emotional feeling via self-report. In Fontaine, J. J. R., Scherer, K. R., & Soriano, C. (eds.), Series in Affective Science. Components of Emotional Meaning: A Sourcebook (pp. 281–298). Oxford: Oxford University Press.Google Scholar

Schrader, C., Brich, J., Frommel, J., Riemer, V., & Rogers, K. (2017). Rising to the challenge: An emotion-driven approach toward adaptive serious games. In Ma, M., & Oikonomou, A. (eds.), Serious Games and Edutainment Applications (pp. 3-28). Switzerland: Springer.Google Scholar

Schrader, C., & Kalyuga, S. (2020). Linking students’ emotions to engagement and writing performance when learning Japanese letters with a pen-based tablet: An investigation based on individual pen-pressure parameters. International Journal of Human–Computer Studies, 135, 1–11.Google Scholar

Schrader, C., & Nett, U. (2018). The perception of control as a predictor of emotional trends during gameplay. Learning and Instruction, 54, 62–72.Google Scholar

Schukajlow, S., Rakoczy, K., & Pekrun, R. (2017). Emotions and motivation in mathematics education: Theoretical considerations and empirical contributions. ZDM: The International Journal on Mathematics Education, 49(3), 307–322.Google Scholar

Schunk, D. H. (1989). Self-efficacy and cognitive achievement: Implications for students with learning problems. Journal of Learning Disabilities, 22(1), 14–22.Google Scholar

Schunk, D. H. (1991). Self-efficacy and academic motivation. Educational Psychologist, 26, 207–231.Google Scholar

Schwarz, N., & Clore, G. L. (1996). Feelings and phenomenal experiences. In Higgins, E. T., & Kruglanski, A. W. (eds.), Social Psychology: Handbook of Basic Principles (pp. 433–465). New York: The Guilford Press.Google Scholar

Seibert, P. S., & Ellis, H. C. (1991). Irrelevant thoughts, emotional mood states, and cognitive task performance. Memory & Cognition, 19(5), 507–513.Google Scholar

Shute, V. J., D’Mello, S., Baker, R., Cho, K., Bosch, N., Ocumpaugh, J., …, Almeda, V. (2015). Modelling how incoming knowledge, persistence, affective states, and in-game progress influence student learning from an educational game. Computers & Education, 86, 224–235.Google Scholar

Song, H. S., Kalet, A. L., & Plass, J. L. (2016). Interplay of prior knowledge, self‐regulation and motivation in complex multimedia learning environments. Journal of Computer Assisted Learning, 32(1), 31–50.Google Scholar

Song, S. H., & Keller, J. M. (2001). Effectiveness of motivationally adaptive computer-assisted instruction on the dynamic aspects of motivation. Educational Technology, Research & Development, 49(2), 5–22.Google Scholar

Spence, D. J., & Usher, E. L. (2007). Engagement with mathematics courseware in traditional and online remedial learning environments: Relationship to self-efficacy and achievement. Journal of Educational Computing Research, 37(3), 267–288.Google Scholar

Stark, L., Malkmus, E., Stark, R., Brünken, R., & Park, B. (2018). Learning-related emotions in multimedia learning: An application of control-value theory. Learning and Instruction, 58, 42–52.Google Scholar

Stephan, M., Gläser-Zikuda, M., & Markus, S. (2019). Students’ achievement emotions and online learning in teacher education. Frontiers in Education, 4(109), 1–21.Google Scholar

Tellegen, A., Watson, D., & Clark, L. A. (1999). On the dimensional and hierarchical structure of affect. Psychological Science, 10(4), 297–303.Google Scholar

Um, E., Plass, J. L., Hayward, E. O., & Homer, B. D. (2012). Emotional design in multimedia learning. Journal of Educational Psychology, 104(2), 485–498.Google Scholar

van Merriënboer, J. J., & Sweller, J. (2005). Cognitive load theory and complex learning: Recent developments and future directions. Educational Psychology Review, 17(2), 147–177.Google Scholar

Wouters, P., van Nimwegen, C., van Oostendorp, H., & van der Spek, E. D. (2013). A meta-analysis of the cognitive and motivational effects of serious games. Journal of Educational Psychology, 105(2), 249–256.Google Scholar

Zhong, B., Qin, Z., Yang, S., Chen, J., Mudrick, N., Taub, M., Azevedo, R. & Lobaton, E. (2017). Emotion recognition with facial expressions and physiological signals. In 2017 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1–8). IEEE.Google Scholar

References

Ainsworth, S. (2014). The multiple representation principle in multimedia learning. In Mayer, R. (ed.), The Cambridge Handbook of Multimedia Learning (2nd ed., pp. 464–486). Cambridge: Cambridge University Press.Google Scholar

Alemdag, E., & Cagiltay, K. (2018). A systematic review of eye tracking research on multimedia learning. Computers & Education, 125, 413–428.Google Scholar

Antonietti, A., Colombo, B., & Di Nuzzo, C. (2014). Metacognition in self-regulated multimedia learning: Integrating behavioural, psychophysiological and introspective measures. Learning Media and Technology, 40, 187–209.Google Scholar

Ayers, P. (2020). Something old, something new from cognitive load theory. Computers in Human Behavior, 113, 1–0.Google Scholar

Azevedo, R. (2005). Computers as metacognitive tools for enhancing learning. Educational Psychologist, 40, 193–197.Google Scholar

Azevedo, R. (2014). Multimedia learning of metacognitive strategies. In. Mayer, R. E. (ed.), The Cambridge Handbook of Multimedia Learning (2nd ed., pp. 647–673). New York: Cambridge University Press.Google Scholar

Azevedo, R. (2020). Reflections on the field of metacognition: Issues challenges, and opportunities. Metacognition and Learning, 15, 91–98.Google Scholar

Azevedo, R., Mudrick, N., Taub, M., & Bradbury, A. (2019). Self-regulation in computer-assisted learning systems. In Dunlosky, J., & Rawson, K. (eds.), Handbook of Cognition and Education (pp. 587–618). New York: Cambridge University Press.Google Scholar

Bohn-Gettler, C. M. (2019). Getting a grip: The PET framework for studying how reader emotions influence comprehension. Discourse Processes, 56, 386–401.Google Scholar

Catrysse, L., Gijbels, D., Donche, V., De Maeyer, S., Lesterhuis, M., & van den Bossche, P. (2018). How are learning strategies reflected in the eyes? Combining results from self-reports and eye-tracking. British Journal of Educational Psychology, 88, 118–137.Google Scholar

Dever, D. A., & Azevedo, R. (2019). Examining gaze behaviors and metacognitive judgments of informational text within game-based learning environments. In Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., & Luckin, R. (eds.), Proceedings of the 20th International Conference on Artificial Intelligence in Education (pp. 121–132). Amsterdam: Springer.Google Scholar

Dunlosky, J., Dudley, D., Spitznage, M. B., & Clements, R. J. (2017). Student’s metamemory knowledge about the impact of stereoscopic three‐dimensional presentations of science content. Applied Cognitive Psychology, 33, 225–233.Google Scholar

Efklides, A., Schwartz, B. L., & Brown, V. (2018). Motivation and affect in self-regulated learning: Does metacognition play a role? In Schunk, D. H., & Greene, J. A. (eds.), Handbook of Self-regulation of Learning and Performance (2nd ed., pp. 64–82). New York: Routledge.Google Scholar

Eitel, A. (2016). How repeated studying and testing affects multimedia learning: Evidence for adaptation to task demands. Learning and Instruction, 41, 70–84.Google Scholar

Fenesi, B., & Kim, J. A. (2014). Learners misperceive the benefits of redundant text in multimedia learning. Frontiers in Psychology, 5, 1–7.Google Scholar

Fiedler, K., & Beier, S. (2014). Affect and cognitive processing in educational contexts. In Pekrun, R., & Linnenbrink-Garcia, L. (eds.), International Handbook of Emotions in Education (pp. 36–55). New York: Taylor & Francis.Google Scholar

Forgas, J. P. (2017). Mood effects on cognition: Affective influences on the content and process of information processing and behavior. In Jeon, M. (ed.), Emotions and Affect in Human Factors and Human–Computer Interaction (pp. 89–122). New York: Academic Press.Google Scholar

Greene, J. A., & Azevedo, R. (2009). A micro-level analysis of SRL processes and their relations to the acquisition of a sophisticated mental model of a complex system. Contemporary Educational Psychology, 34, 18–29.Google Scholar

Greene, J. A., Deekens, V., Copeland, D., & Yu, S. (2018). Capturing and modeling self-regulated learning using think-aloud protocols. In Schunk, D., & Greene, J. A. (eds.), Handbook of Self-regulation of Learning and Performance (2nd ed., pp. 323–337). New York: Routledge.Google Scholar

Hadwin, A., Järvelä, S., & Miller, M. (2018). Self-regulated, co-regulation, and shared regualtion in collaborative learning environments. In Schunk, D., & Greene, J. A. (eds.), Handbook of Self-regulation of Learning and Performance (2nd ed., pp. 83–105). New York: Routledge.Google Scholar

Harley, J. M., Taub, M., Azevedo, R., & Bouchet, F. (2018). Let’s set up some subgoals: Understanding human-pedagogical agent collaborations and their implications for learning and prompt and feedback compliance. IEEE Transactions on Learning Technologies, 11, 54–66.Google Scholar

Hegarty, M. (2014). Multimedia learning and the development of mental models. In Mayer, R. (ed.), Handbook of Multimedia (2nd ed.). Cambridge: Cambridge University Press.Google Scholar

Hoch, E., Scheiter, K., & Schuler, A. (2020). Implementation intentions for improving self-regulation in multimedia learning: Why don’t they work? The Journal of Experimental Education, 88, 536–558.Google Scholar

Jaeger, A., & Wiley, J. (2014). Do illustrations help or harm metacomprehension accuracy? Learning and Instructions, 34, 58–73.Google Scholar

Klepsch, M., & Seufert, T. (2020). Understanding instructional design effects by differentiated measurement of intrinsic, extraneous, and germane cognitive load. Instructional Science, 48, 45–77.Google Scholar

Kramarski, B., & Friedman, S. (2014). Solicited versus unsolicited metacognitive prompts for fostering mathematical problem-solving using multimedia. Journal of Educational Computing Research, 50, 285–314.Google Scholar

Lajoie, S. P., & Azevedo, R. (2006). Teaching and learning in technology-rich environments. In Alexander, P., & Winne, P. (eds.), Handbook of Educational Psychology (2nd ed., pp. 803–821). Mahwah, NJ: Erlbaum.Google Scholar

Lehman, J., Goussios, C., & Seufert, T. (2016). Working memory capacity and disfluency effect: An aptitude-treatment-interaction study. Metacognition and Learning, 11, 89–105.Google Scholar

Li, J., Antonenko, P., & Wang, J. (2019). Trends and issues in multimedia learning research in 1996–2016: A bibliometric analysis. Educational Research Review, 28, 1–21.Google Scholar

Matton, N., Vrignaud, C., Rouillard, Y., & Lemarié, J. (2018). Learning flight procedures by enacting and receiving feedback. Applied Ergonomics, 70, 253–259.Google Scholar

Mayer, R. E. (2001). Multimedia Learning. New York: Cambridge University Press.Google Scholar

Mayer, R. E. (2009). Multimedia Learning (2nd ed.). New York: Cambridge University Press.Google Scholar

Mayer, R. E. (ed.) (2014). The Cambridge Handbook of Multimedia Learning. New York: Cambridge University Press.Google Scholar

Mayer, R. E. (2019). Thirty years of research on online research. Applied Cognitive Psychology, 33, 152–159.Google Scholar

McCardle, L., & Hadwin, A. F. (2015). Using multiple, contextualized data sources to measure learners’ perceptions of their self-regulated learning. Metacognition and Learning, 10, 43–75.Google Scholar

Mevarech, Z. R., Verschaffel, L., & De Corte, E. (2018). Metacognitive pedagogies in mathematics classrooms: From kindergarten to college and beyond. In Schunk, D. H., & Greene, J. A. (eds.), Handbook of Self-regulation of Learning and Performance (2nd ed., pp. 109–123). New York: Routledge.Google Scholar

Moreno, R. (2006). Learing in high-tech and multimedia environments. Current Directions in Psychological Science, 15, 63–67.Google Scholar

Mudrick, N. V., Azevedo, R., & Taub, M. (2019). Integrating metacognitive judgments and eye movements using sequential pattern mining to understand processes underlying multimedia learning. Computers in Human Behavior, 96, 223–234.Google Scholar

Nelson, T., & Narens, L. (1990). Metamemory: A Theoretical Framework and New Findings. The Psychology of Learning and Motivation (pp. 125–173). New York: Academic Press.Google Scholar

Panadero, E. (2017). A review of self-regulated learning: Six models and four directions for research. Frontiers in Psychology, 8, 1–28.Google Scholar

Paulson, E. J., & Bauer, L. (2011). Goal setting as an explicit element of metacognitive reading and study strategies for college readers. National Association for Developmental Education Digest, 5, 41–49.Google Scholar

Pilegard, C., & Mayer, R. (2015). Adding judgments of understanding to the metacognitive toolbox. Learning and Individual Differences, 41, 62–72.Google Scholar

Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In Boekaerts, M., Pintrich, P. R., & Zeidner, M. (eds.), Handbook of Self-regulation (pp. 452–502). San Diego, CA: Academic Press.Google Scholar

Riemer, V., & Schrader, C. (2019). Mental model development in multimedia learning: Interrelated effects of emotions and self-monitoring. Frontiers in Psychology, 10, https://doi.org/10.3389/fpsyg.2019.00899.Google Scholar

Scheiter, K., Schubert, C., & Schüler, A. (2018). Self-regulated learning from illustrated text: Eye movement modelling to support use and regulation of cognitive processes during learning from multimedia. British Journal of Educational Psychology, 88, 80–94.Google Scholar

Schunk, D. H., & Greene, J. A. (eds.) (2018). Handbook of Self-regulation of Learning and Performance (2nd ed.). New York: Routledge.Google Scholar

Stark, L., Brünken, R., & Park, B. (2018). Emotional text design in multimedia learning: A mixed-methods study using eye tracking. Computers & Education, 120, 185–196.Google Scholar

Strobel, B., Lindner, M. A., Saß, S., & Köller, O. (2018). Task-irrelevant data impair processing of graph reading tasks: An eye tracking study. Learning and Instruction, 55, 139–147.Google Scholar

Sweller, J. (2020). Cognitive load theory and educational technology. Educational Technology Research and Development, 68, 1–16.Google Scholar

Tarricone, P. (2011). The Taxonomy of Metacognition. New York: Psychology Press.Google Scholar

Taub, M., & Azevedo, R. (2019). How does prior knowledge influence eye fixations and sequences of cognitive and metacognitive SRL processes during learning with an intelligent tutoring system? International Journal of Artificial Intelligence in Education, 29, 1–28.Google Scholar

Usher, E. L., & Schunk, D. H. (2018). Social cognitive theoretical perspective of self-regulation. In Schunk, D. H., & Greene, J. A. (eds.), Handbook of Self-regulation of Learning and Performance (2nd ed., pp. 19–35). New York: Routledge.Google Scholar

Winne, P. H. (2018). Cognition and metacognition withing self-regulated learning. In Schunk, D., & Greene, J. A. (eds.), Handbook of Self-regulation of Learning and Performance (2nd ed., pp. 36–48). New York: Routledge.Google Scholar

Winne, P. H. (2019). Paradigmatic dimensions of instrumentation and analytic methods in research on self-regulated learning. Computers in Human Behavior, 96, 285–289.Google Scholar

Winne, P. H., & Azevedo, R. (in press). Metacognition. In Sawyer, K. (ed.), Cambridge Handbook of the Learning Sciences (3rd ed., pp. 63–87). Cambridge: Cambridge University Press.Google Scholar

Winne, P. H., & Hadwin, A. F. (2008). The weave of motivation and self-regulated learning. In Schunk, D. H., & Zimmerman, B. J. (eds.), Motivation and Self-Regulated Learning: Theory, Research, and Applications (pp. 297–314). Mahwah, NJ: Erlbaum.Google Scholar

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Part II - Theoretical Foundations

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