Metacognition in Multimedia Learning

doi:10.1017/9781108894333.013

10 - Metacognition in Multimedia Learning

from Part II - Theoretical Foundations

Published online by Cambridge University Press: 19 November 2021

Roger Azevedo and

Daryn Dever

Edited by

Richard E. Mayer and

Logan Fiorella

Show author details

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

Book contents

Get access

Summary

Cognitive and metacognitive strategies are key to successful learning with multimedia; however, research shows that learners rarely use these strategies effectively and consequently fail to develop a deep understanding of complex topics and domains. Dynamically and accurately monitoring and regulating one’s own cognitive and metacognitive strategies is necessary to be a successful learner but demands an enormous amount of effort. In this chapter, we (1) define metacognitive strategies during multimedia learning; (2) review recent empirical literature on metacognitive strategies during multimedia learning; (3) present a new model of multimedia learning ; (4) provide recommendations for augmenting contemporary cognitive theories of multimedia learning to account for metacognition; (5) propose empirically-based principles for designing multimedia environments aimed at fostering metacognitive strategies; and finally (6) highlight directions for future research.

Keywords

cognitive strategies metacognitive strategies self-regulated learning metacognitive model of multimedia learning monitoring regulating

Information

Type: Chapter
Information: The Cambridge Handbook of Multimedia Learning , pp. 132 - 142
DOI: https://doi.org/10.1017/9781108894333.013 [Opens in a new window]
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.)

Book purchase

Temporarily unavailable

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.CrossRef 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.CrossRef 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.CrossRef 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

Accessibility standard: WCAG 2.2 AAA

Why this information is here

This section outlines the accessibility features of this content - including support for screen readers, full keyboard navigation and high-contrast display options. This may not be relevant for you.

Accessibility Information

The HTML of this chapter complies with version 2.2 of the Web Content Accessibility Guidelines (WCAG), offering more comprehensive accessibility measures for a broad range of users and attains the highest (AAA) level of WCAG compliance, optimising the user experience by meeting the most extensive accessibility guidelines.

Content Navigation

Table of contents navigation
Allows you to navigate directly to chapters, sections, or non‐text items through a linked table of contents, reducing the need for extensive scrolling.

Index navigation
Provides an interactive index, letting you go straight to where a term or subject appears in the text without manual searching.

Reading Order & Textual Equivalents

Single logical reading order
You will encounter all content (including footnotes, captions, etc.) in a clear, sequential flow, making it easier to follow with assistive tools like screen readers.

Full alternative textual descriptions
You get more than just short alt text: you have comprehensive text equivalents, transcripts, captions, or audio descriptions for substantial non‐text content, which is especially helpful for complex visuals or multimedia.

Visualised data also available as non-graphical data
You can access graphs or charts in a text or tabular format, so you are not excluded if you cannot process visual displays.

Visual Accessibility

Use of high contrast between text and background colour
You benefit from high‐contrast text, which improves legibility if you have low vision or if you are reading in less‐than‐ideal lighting conditions.