Skip to main content Accessibility help

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

Close cookie message
×
Hostname: page-component-586b7cd67f-gb8f7 Total loading time: 0 Render date: 2024-11-24T17:44:09.519Z Has data issue: false hasContentIssue false

39 - Multimedia Learning with Simulations

from Part VIII - Multimedia Learning with Media

Published online by Cambridge University Press:  19 November 2021

By
Susanne P. Lajoie
Edited by
Richard E. Mayer  and
Logan Fiorella
Richard E. Mayer
Affiliation:
University of California, Santa Barbara
Logan Fiorella
Affiliation:
University of Georgia
Get access

Summary

There has been an explosion in the uses of multimedia and their various platforms. The proliferation of different types of technology inclusion in education has become even greater due to the increased need for remote platforms for education globally. My focus in this paper is on providing a definition of multimedia learning with simulations. There are many types of simulations and this chapter presents a framework for understanding this diversity. In particular, I discuss the multimedia principles that inform the design of simulations along with research evidence of how simulations support learning. Future directions for this research are discussed.

Keywords

simulationsdeliberate practicefidelityimmersive multimediaanimationrole-play simulations
Type
Chapter
Information
The Cambridge Handbook of Multimedia Learning , pp. 461 - 471
Publisher: Cambridge University Press
Print publication year: 2021

Access options

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

References

Ainsworth, S. (2006). DeFT: A conceptual framework for considering learning with multiple representations. Learning and Instruction, 16, 183–198.Google Scholar
AlZhrani, G., Alotaibi, F., Azarnoush, H. M., Winkler-Schwartz, A., Sabbagh, A., Lajoie, S. P., & Del Maestro, R. F. (2015). Proficiency performance benchmarks for removal of simulated brain tumors using “NeuroTouch” a virtual reality simulator. Journal of Surgical Education, 72(4), 685–696.Google Scholar
Azevedo, R., & Cromley, J. G. (2004). Does training on self-regulated learning facilitate students’ learning with hypermedia? Journal of Educational Psychology, 96, 523–535.Google Scholar
Azevedo, R., & Feyzi-Behnagh, R. (2011). Dysregulated learning with advanced learning technologies. Journal of e-Learning and Knowledge Society, 7(2), 9–18.Google Scholar
Azevedo, R., Mudrick, N. V., Taub, M., & Bradbury, A. E. (2019). Self-regulation in computer-assisted learning systems. In Dunlosky, J., & Rawson, K. A. (eds.), The Cambridge Handbook of Cognition and Education (pp. 587–618). Cambridge: Cambridge University Press.Google Scholar
Bannert, M., & Reimann, P. (2012). Supporting self-regulated hypermedia learning through prompts. Instructional Science, 40, 193–211.CrossRefGoogle Scholar
Birchfield, D., Thornburg, H., Megowan-Romanowicz, M. C., Hatton, S., Mechtley, B., Dolgov, I., & Burleson, W. (2008). Embodiment, multimodality, and composition: Convergent themes across HCI and education for mixed-reality learning environments. Advances in Human–Computer Interaction, 2008, 874563.Google Scholar
Bransford, J., Brown, A. L., & Cocking, R. R. (2000). How People Learn: Brain, Mind, Experience, and School (expanded ed.). Commission on Behavioral and Social Sciences and Education, National Research Council. Washington, DC: The National Academies Press.Google Scholar
Burbules, N. C. (2006). Rethinking the virtual. In Weiss, J., Nolan, J., Hunsinger, J., & Trifonas, P. (eds.), The International Handbook of Virtual Learning Environments (pp. 37–58). Dordrecht: Springer.Google Scholar
Clark, J. M., & Paivio, A. (1991). Dual coding theory and education. Educational Psychology, 3(3), 149–210.Google Scholar
Delorme, S., Laroche, D., DiRaddo, R., & Del Maestro, R. F. (2012). NeuroTouch: A physics-based virtual simulator for cranial microneurosurgery training. Neurosurgery, 71(1 Suppl Operative), 32–42.Google Scholar
Duffy, M. C., Azevedo, R., Sun, N., Griscom, S., Stead, V., Crelinsten, L., Wiseman, J., Maniatis, T., & Lachapelle, K. (2015). Team regulation in a simulated medical emergency: An in-depth analysis of cognitive, metacognitive, and affective processes. Instructional Science, 43, 401–426.CrossRefGoogle Scholar
Duffy, M. C., Lajoie, S. P., Pekrun, R., & Lachapelle, K. (2020). Emotions in medical education: Examining the validity of the Medical Emotion Scale (MES) across authentic medical learning environments. Journal of Learning and Instruction, 70, 101150.CrossRefGoogle Scholar
Ericsson, K. A., Krampe, R. T., & Tesch-Romer, C. (1993). The role of deliberate practice in the acquisition of expert performance. Psychological Review, 100(3), 363–406.Google Scholar
Goldman, S. R. (2003). Learning in complex domains: When and why do multiple representations help? Learning and Instruction, 13(2), 239–244.Google Scholar
Graesser, A. (2020). Emotions are the experiential glue of learning environments in the 21st century. Learning and Instruction, 70, 101212.Google Scholar
Greeno, J. G. (1998). The situativity of knowing, learning, and research. American Psychologist, 53(1), 5–26 .Google Scholar
Hadwin, A. F., Järvelä., S., & Miller, M. (2018). Self-regulation, co-regulation, and shared regulation in collaborative learning environments. In Schunk, D. H., & Greene, J. A. (eds.), Handbook of Self-regulation of Learning and Performance (2nd ed., pp. 83–106). Abingdon: Routledge.Google Scholar
Issa, N., Mayer, R. E., Schuller, M., Wang, E., Shapiro, M. B., & Darosa, D. A. (2013). Teaching for understanding in medical classrooms using multimedia design principles. Medical Education, 47(4), 388–396.CrossRefGoogle ScholarPubMed
Järvelä, S., Järvenoja, H., Malmberg, J., Isohätälä, J., & Sobocinski, M.(2016). How do types of interaction and phases of self-regulated learning set a stage for collaborative engagement? Learning and Instruction, 43, 39–51.Google Scholar
Järvenoja, H., Järvelä, S., & Malmberg, J. (2020). Supporting groups’ emotion and motivation regulation during collaborative learning. Learning and Instruction, 70, 101090.CrossRefGoogle Scholar
Kuang, X., Eysink, T. H., & de Jong, T. (2020). Effects of providing partial hypotheses as a support for simulation‐based inquiry learning. Journal of Computer Assisted Learning, 36(4), 487–501.Google Scholar
Lajoie, S. P. (2014). Multimedia learning of cognitive processes. In Mayer, R. E. (ed.) The Cambridge Handbook of Multimedia Learning (2nd ed., pp. 623–646). Cambridge: Cambridge University Press.Google Scholar
Lajoie, S. P., Cruz-Panesso, I., & Lachapelle, K. (2015). Learning in the health sector with simulated systems. In Spector, M. (ed.), Encyclopedia of Educational Technology (pp. 470–472). Thousand Oaks, CA: Sage.Google Scholar
Lajoie, S. P. & Li, S. (submitted). Interface designs applied to AIED learning and teaching environments. In. du Boulay, B., Mitrovic, A., & Yacef, K. (eds.), Handbook of Artificial Intelligence in Education. Cheltenham: Edward Elgar Press.Google Scholar
Lajoie, S. P., & Nakamura, C. (2005). Multimedia learning of cognitive skills. In Mayer, R. (ed.), Cambridge Handbook of Multimedia Learning (pp. 489–504). Cambridge: Cambridge University Press.Google Scholar
Lajoie, S. P., Pekrun, R., Azevedo, R., & Leighton, J. P. (2020). Understanding and measuring emotions in technology-rich learning environments. Journal of Learning and Instruction, 70, 101272.Google Scholar
Lajoie, S. P., & Poitras, E. (2017). Crossing disciplinary boundaries to improve technology rich learning. Teachers College Record, 119(3), 1–30.Google Scholar
Loderer, K., Pekrun, R., & Lester, J. C. (2020). Beyond cold technology: A systematic review and meta-analysis on emotions in technology-based learning environments, Learning and Instruction, 70, 101162.Google Scholar
Liu, C., Calvo, R., & Lim, R. (2016b). Improving medical students’ awareness of their nonverbal communication through automated nonverbal behavior feedback. Frontiers in ICT, 3(11).CrossRefGoogle Scholar
Liu, C., Lim, R., McCabe, K., Taylor, S., & Calvo, R. (2016a). A web-based telehealth training platform incorporating automated non-verbal behavior feedback for teaching communication skills to medical students: A randomized crossover study. Journal of Medical Internet Research, 18(9), e246.Google Scholar
Makransky, G., Borre‐Gude, S., & Mayer, R. E. (2019). Motivational and cognitive benefits of training in immersive virtual reality based on multiple assessments. Journal of Computer Assisted Learning, 35(6), 691–707.CrossRefGoogle Scholar
Mavin, T. J., & Murray, P. S. (2010). The development of airline pilot skills through simulated practice. In Billett, S. (ed.), Learning through Practice. Professional and Practice-based Learning (Vol 1, pp. 268–286). Heidelberg: Springer.Google Scholar
Mayer, R. E. (2003). The promise of multimedia learning: Using the same instructional design methods across different media. Learning and Instruction, 13(2), 125–139.Google Scholar
Mayer, R. E. (2014). Computer Games for Learning: An Evidence-based Approach. Cambridge, MA: MIT Press.Google Scholar
Mayer, R. E. (2020a). Multimedia Learning (3rd ed.). New York: Cambridge University Press.Google Scholar
Mayer, R. E. (2020b). Searching for the role of emotions in e-learning. Learning and Instruction, 70, 101213.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(2), 312–320.Google Scholar
McLean, G. M., Lambeth, S., & Mavin, T. (2016). The use of simulation in ab initio pilot training. The International Journal of Aviation Psychology, 26(1–2), 36–45.Google Scholar
Merchant, Z., Goetz, E. T., Cifuentes, L., Keeney-Kennicutt, W., & Davis, T. J. (2014). Effectiveness of virtual reality-based instruction on students’ learning outcomes in K-12 and higher education: A meta-analysis. Computers and Education, 70, 29–40.Google Scholar
Merriam-Webster. (n.d.). Simulation. In Merriam-Webster.com dictionary. Available from www.merriam-webster.com/dictionary/simulation (last accessed September 22, 2020).Google Scholar
Mirchi, N., Bissonnette, V., Yilmaz, R., Ledwos, N., Winkler-Schwartz, A., & Del Maestro, R. F. (2020). The virtual operative assistant: An explainable artificial intelligence tool for simulation based training in surgery and medicine. PLoS ONE, 15(2), e0229596.Google Scholar
Olympiou, G., Zacharias, Z., & Dejong, T. (2013). Making the invisible visible: Enhancing students’ conceptual understanding by introducing representations of abstract objects in a simulation. Instructional Science, 41(3), 575–596.CrossRefGoogle Scholar
Papert, S. (1987), Microworlds: Transforming education. In Lawler, R., & Yazsani, M. (eds.), Artificial Intelligence and Education Learning Environments and Tutoring Systems (pp. 79–94). New York: Ablex Publishers.Google Scholar
Parong, J., & Mayer, R. E. (2021). Cognitive and affective processes for learning science in immersive virtual reality. Journal of Computer Assisted Learning, 37, 226–241.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: Routledge.Google Scholar
Platts, D., Anderson, B., Forshaw, T., & Burstow, D. (2011). Use of an echocardiographic mannequin simulator for early-sonographer training. Heart, Lung and Circulation, 20, S199–S200.Google Scholar
Reed, S. K. ( 2010). Cognitive architectures for multimedia learning. Educational Psychologist, 41(2), 87–98.Google Scholar
Rowe, J. P., Shores, L. R., Mott, B. W., & Lester, J. C. (2011). Integrating learning, problem solving, and engagement in narrative-centered learning environments. International Journal of Artificial Intelligence in Education, 21(1–2), 115–133.Google Scholar
Sabourin, J. L., Rowe, J. P., Mott, B. W., & Lester, J. C. (2013). Considering alternate futures to classify off-task behavior as emotion self-regulation: A supervised learning approach. Journal of Educational Data Mining, 5(1), 9–38.Google Scholar
Salas, E., Bowers, C. A., & Rhodenizer, L. (1998). It is not how much you have but how you use it: Toward a rational use of simulation to support aviation training. The International Journal of Aviation Psychology, 8(3), 197–208.Google Scholar
Shute, V., Rahimi, S., Smith, G., Ke, F., Almond, R., Dai, C., Kuba, R., Liu, Z., Yang, X., & Sun, C. (2021). Maximizing learning without sacrificing the fun: Stealth assessment, adaptivity and learning supports in educational games. Journal of Computer Assisted Learning, 37, 127–141.Google Scholar
Shute, V., & Ventura, M. (2013). Measuring and Supporting Learning in Games: Stealth Assessment. Cambridge, MA: The MIT Press.Google Scholar
Sroka, G., Feldman, L. S., Vassiliou, M. C., Kaneva, P. A., Fayez, R., & Fried, G. M. (2010). Fundamentals of laparoscopic surgery simulator training to proficiency improves laparoscopic performance in the operating room – A randomized controlled trial. American Journal of Surgery, 199(1), 115–120.Google Scholar
Sweller, J. (1994). Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction, 4(4), 295–312.Google Scholar
Taub, M., Sawyer, R., Smith, A., Rowe, J., Azevedo, R., & Lester, J. (2020). The agency effect: The impact of student agency on learning, emotions, and problem-solving behaviors in a game-based learning environment. Computers & Education, 147, 103781.Google Scholar
Vassiliou, M. C., Feldman, L. S., Andrew, C. G., Bergman, S., Leffondré, K., Stanbridge, D., & Fried, G. M. (2005). A global assessment tool for evaluation of intraoperative laparoscopic skills. American Journal of Surgery, 190, 107–113.Google Scholar
Wiseman, J., Blanchard, E. G., & Lajoie, S. P. (2016). The deteriorating patient smartphone app: Towards serious game design. In Bridges, S., Chan, L. K., & Hmelo-Silver, C. (eds.), Educational Technologies in Medical and Health Sciences Education (pp. 215-234). New York: Springer.Google Scholar
Wiseman, J., & Snell, L. (2008). The deteriorating patient: A realistic but “low‐tech” simulation of emergency decision‐making. Clinical Teacher, 5(2), 93–97.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

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

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

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

Available formats
×

Save book to Google Drive

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

Available formats
×