Book contents
- The Cambridge Handbook of Multimedia Learning
- The Cambridge Handbook of Multimedia Learning
- Copyright page
- Contents
- Figures
- Tables
- Contributors
- Preface
- Acknowledgments
- Part I Background
- Part II Theoretical Foundations
- Part III Basic Principles of Multimedia Learning
- Part IV Principles for Reducing Extraneous Processing in Multimedia Learning
- Part V Principles for Managing Essential Processing in Multimedia Learning
- Part VI Principles Based on Social and Affective Features of Multimedia Learning
- Part VII Principles Based on Generative Activity in Multimedia Learning
- 28 The Generative Activity Principle in Multimedia Learning
- 29 The Mapping Principle in Multimedia Learning
- 30 The Drawing Principle in Multimedia Learning
- 31 The Imagination Principle in Multimedia Learning
- 32 The Self-Explanation Principle in Multimedia Learning
- 33 The Guided Inquiry Principle in Multimedia Learning
- 34 The Feedback Principle in Multimedia Learning
- 35 The Learner Control Principle in Multimedia Learning
- 36 The Cognitive Load Self-Management Principle in Multimedia Learning
- Part VIII Multimedia Learning with Media
- Author Index
- Subject Index
- References
35 - The Learner Control Principle in Multimedia Learning
from Part VII - Principles Based on Generative Activity in Multimedia Learning
Published online by Cambridge University Press: 19 November 2021
Book contents
- The Cambridge Handbook of Multimedia Learning
- The Cambridge Handbook of Multimedia Learning
- Copyright page
- Contents
- Figures
- Tables
- Contributors
- Preface
- Acknowledgments
- Part I Background
- Part II Theoretical Foundations
- Part III Basic Principles of Multimedia Learning
- Part IV Principles for Reducing Extraneous Processing in Multimedia Learning
- Part V Principles for Managing Essential Processing in Multimedia Learning
- Part VI Principles Based on Social and Affective Features of Multimedia Learning
- Part VII Principles Based on Generative Activity in Multimedia Learning
- 28 The Generative Activity Principle in Multimedia Learning
- 29 The Mapping Principle in Multimedia Learning
- 30 The Drawing Principle in Multimedia Learning
- 31 The Imagination Principle in Multimedia Learning
- 32 The Self-Explanation Principle in Multimedia Learning
- 33 The Guided Inquiry Principle in Multimedia Learning
- 34 The Feedback Principle in Multimedia Learning
- 35 The Learner Control Principle in Multimedia Learning
- 36 The Cognitive Load Self-Management Principle in Multimedia Learning
- Part VIII Multimedia Learning with Media
- Author Index
- Subject Index
- References
Summary
The learner control principle is that giving learners control over their instruction by allowing them to pace, sequence, and select information aids learning if learners possess high levels of prior knowledge and if they receive additional instructional support to orient themselves in the learning environment and to self-regulate their learning. Learner control has been suggested to afford an active, constructive processing of instruction, to increase and sustain the motivation to learn, to enhance the acquisition of self-regulatory skills, and to enable learners to adapt instruction to their preferences and needs. Despite these envisioned benefits, there is little empirical evidence supporting these claims, which is largely because these benefits are overwritten by the additional cognitive and metacognitive demands learner control imposes.
Keywords
- Type
- Chapter
- Information
- The Cambridge Handbook of Multimedia Learning , pp. 418 - 429Publisher: Cambridge University PressPrint publication year: 2021
References
Amadieu, F., van Gog, T., Paas, F., Tricot, A., & Mariné, C. (2009). Effects of prior knowledge and concept-map structure on disorientation, cognitive load, and learning. Learning and Instruction, 19, 376–386.Google Scholar
Azevedo, R. (2005). Using hypermedia as a metacognitive tool for enhancing student learning? The role of self-regulated learning. Educational Psychologist, 40, 199–209.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.CrossRefGoogle Scholar
Azevedo, R., Cromley, J. G., & Seibert, D. (2004). Does adaptive scaffolding facilitate students’ ability to regulate their learning with hypermedia? Contemporary Educational Psychology, 29, 344–370.Google Scholar
Azevedo, R., Cromley, J. G., Winters, F. I., Moos, D. C., & Greene, J. A. (2006). Using computers as metacognitive tools to foster students’ self-regulated learning. Technology, Instruction, Cognition, and Learning Journal, 3, 97–104.Google Scholar
Azevedo, R., Guthrie, J. T., & Seibert, D. (2004). The role of self-regulated learning in fostering students’ conceptual understanding of complex systems with hypermedia. Journal of Educational Computing Research, 30, 87–111.Google Scholar
Azevedo, R., & Hadwin, A. F. (2005). Scaffolding self-regulated learning and metacognition: Implications for the design of computer-based scaffolds. Instructional Science, 33, 367–379.Google Scholar
Azevedo, R., Johnson, A., Chauncey, A., & Burkett, C. (2010). Self-regulated learning with MetaTutor: Advancing the science of learning with MetaCognitive tools. In Khine, M., & Saleh, I. (eds.), New Science of Learning: Computers, Cognition, and Collaboration in Education (pp. 225–247). Amsterdam: Springer.Google Scholar
Azevedo, R., Moos, D. C., Johnson, A. M., & Chauncey, A. D. (2010). Measuring cognitive and metacognitive regulatory processes during hypermedia learning: Issues and challenges. Educational Psychologist, 45, 210–223.CrossRefGoogle Scholar
Baddeley, A. D. (2007). Working Memory, Thought and Action. Oxford: Oxford University Press.Google Scholar
Bannert, M. (2006). Effects of reflection prompts when learning with hypermedia. Journal of Educational Computing Research, 4, 359–375.Google Scholar
Bannert, M., & Reimann, P. (2012). Supporting self-regulated hypermedia learning through prompts. Instructional Science, 40, 193–211.Google Scholar
Bannert, M., Sonnenberg, C., Mengelkamp, C., & Pieger, E. (2015). Short- and long-term effects of students’ self-directed metacognitive prompts on navigation behavior and learning performance. Computers in Human Behavior, 52, 293–306.Google Scholar
Baumeister, R. F., Muraven, M., & Tice, D. M. (2000). Ego depletion: A resource model of volition, self- regulation, and controlled processing. Social Cognition, 18, 130–150.Google Scholar
Betrancourt, M. (2005). The animation and interactivity principles in multimedia learning. In Mayer, R. E. (ed.), The Cambridge Handbook of Multimedia Learning (pp. 287–296). New York: Cambridge University Press.Google Scholar
Chen, C., & Rada, R. (1996). Interacting with hypertext: A meta-analysis of experimental studies. Human–Computer Interaction, 11, 125–156.CrossRefGoogle Scholar
Chen, S. Y., Fan, J.-P., & Macredie, R. D. (2006). Navigation in hypermedia learning systems: Experts vs. novices. Computers in Human Behavior, 22, 251–266.Google Scholar
Chi, M. T. H., & Wylie, R. (2014). The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational Psychologist, 49, 219–243.CrossRefGoogle 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
de Bruin, A. B. H., Roelle, J., Baars, M., & EFG-MRE. (2020). Synthesizing cognitive load and self-regulation theory: A theoretical framework and research agenda. Educational Psychology Review, 32, 903–915.CrossRefGoogle Scholar
Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 4, 227–268.Google Scholar
DeStefano, D., & LeFevre, J.-A. (2007). Cognitive load in hypertext reading: A review. Computers in Human Behavior, 23, 1616–1641.Google Scholar
Dias, P., Gomes, M. J., & Correia, A. P. (1999). Disorientation in hypermedia environments: Mechanisms to support navigation. Journal of Educational Computing Research, 20, 93–117.Google Scholar
Dillon, A., & Gabbard, R. (1998). Hypermedia as an educational technology: A review of the quantitative research literature on learner comprehension, control, and style. Review of Educational Research, 68, 322–349.Google Scholar
Eitel, A., Endres, T., & Renkl, A. (2020). Self-management as a bridge between cognitive load and self-regulated learning: The illustrative case of seductive details. Educational Psychology Review, 32, 1073–1087.Google Scholar
Gall, J. E., & Hannafin, M. J. (1994). A framework for the study of hypertext. Instructional Science, 22, 207–232.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, 33–41.CrossRefGoogle Scholar
Hummel, H. G. K., Nadolski, R. J., Eshuis, J., Slootmaker, A., & Storm, J. (2020). Serious game in introductory psychology for professional awareness: Optimal learner control and authenticity. British Journal of Educational Technology, 52(1), 125–141.Google Scholar
Jacobson, M. J., Maouri, C., Mishra, P., & Kolar, C. (1995). Learning with hypertext learning environments: Theory, design, and research. Journal of Educational Multimedia and Hypermedia, 4, 321–364.Google Scholar
Johnson, A. M., Azevedo, R., & D’Mello, S. K. (2011). The temporal and dynamic nature of self-regulatory processes during independent and externally assisted hypermedia. Cognition and Instruction, 29, 471–504.Google Scholar
Kalyuga, S. (2007). Expertise reversal effect and its implications for learner-tailored instruction. Educational Psychology Review, 19, 509–539.Google Scholar
Katz, I., & Assor, A. (2007). When choice motivates and when it does not. Educational Psychology Review, 19, 429–442.Google Scholar
Kauffman, D. F. (2004). Self-regulated learning in web-based environments: Instructional tool designed to facilitate cognitive strategy use, metacognitive processing and motivational beliefs. Journal of Educational Computing Research, 30, 139–161.Google Scholar
Kennedy, G. (2004). Promoting cognition in multimedia interactivity research. Journal of Interactive Learning Research, 15, 43–61.Google Scholar
Kintsch, W. (1998). Comprehension: A Paradigm for Cognition. Cambridge: Cambridge University Press.Google Scholar
Kirschner, P. A., & van Merriënboer, J. J. G. (2013). Do learners really know best? Urban legends in education. Educational Psychologist, 48, 169–183.Google Scholar
Lawless, K. A., Brown, S. W., Mills, R., & Mayall, H. J. (2003). Knowledge, interest, recall, and navigation: A look at hypertext processing. Journal of Literacy Research, 35, 911–934.Google Scholar
Lawless, K. A., & Kulikowich, J. M. (1998). Domain knowledge, interest and hypertext navigation: A study of individual differences. Journal of Educational Multimedia and Hypermedia, 7, 51–69.Google Scholar
Lin, X., & Lehman, J. (1999). Supporting learning of variable control in a computer-based biology environment: Effects of promoting college students to reflect on their own thinking. Journal of Research in Science Teaching, 36, 837–858.Google Scholar
Lowrey, W., & Kim, K. S. (2009). Online news media and advanced learning: A test of cognitive flexibility theory. Journal of Broadcasting and Electronic Media, 53, 547–566.Google Scholar
Lunts, E. (2002). What does the literature say about the effectiveness of learner control in computer-assisted instruction? Electronic Journal for the Integration of Technology in Education, 1, 59–75.Google Scholar
Mayer, R. E. (ed.) (2014). The Cambridge Handbook of Multimedia Learning (2nd ed.). New York: Cambridge University Press.Google Scholar
McDonald, S., & Stevenson, R. J. (1996). Disorientation in hypertext: The effects of three text structures on navigation performance. Applied Ergonomics, 27, 61–68.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
Metcalfe, J. (2002). Is study time allocated selectively to a region of proximal learning? Journal of Experimental Psychology: General, 131, 349–363.Google Scholar
Mihalca, L., Mengelkamp, C., & Schnotz, W. (2017). Accuracy of metacognitive judgments as a moderator of learner control effectiveness in problem-solving tasks. Metacognition and Learning, 12, 357–379.Google Scholar
Moos, D. C., & Azevedo, R. (2008). Self-regulated learning with hypermedia: The role of prior domain knowledge. Contemporary Educational Psychology, 33, 270–298.CrossRefGoogle Scholar
Moos, D. C., & Marroquin, E. (2010). Multimedia, hypermedia, and hypertext: Motivation considered and reconsidered. Computers in Human Behavior, 26, 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
Moritz, J., Meyerhoff, H. S., & Schwan, S. (2020). Control over spatial representation format enhances information extraction but prevents long-term learning. Journal of Educational Psychology, 112, 148–165.Google Scholar
Müller, N. M., & Seufert, T. (2018). Effects of self-regulation prompts in hypermedia learning on learning performance and self-efficacy. Learning and Instruction, 58, 1–11.Google Scholar
Nelson, T. O., & Narens, L. (1990). Metamemory: A theoretical framework and new findings. In Bower, G. (ed.), The Psychology of Learning and Motivation: Advances in Research and Theory (Vol. 26, pp. 125–173). San Diego: Academic Press.Google Scholar
Niederhauser, D. S., Reynolds, R. E., Salmen, D. L., & Skolmoski, P. (2000). The influence of cognitive load on learning from hypertext. Journal of Educational Computing Research, 23, 237–255.Google Scholar
Niemiec, R. P., Sikorski, C., & Walberg, H. J. (1996). Learner-control effects: A review of reviews and a meta-analysis. Journal of Educational Computing Research, 15, 157–174.Google Scholar
Pashler, H., McDaniel, M., Rohrer, D. R., & Bjork, R. (2008). Learning styles: Concepts and evidence. Psychological Science in the Public Interest, 9, 105–119.Google Scholar
Patall, E. A., Cooper, H., & Robinson, J. C. (2008). The effects of choice on intrinsic motivation and related outcomes: A meta-analysis of research findings. Psychological Bulletin, 134, 270–300.Google Scholar
Pieger, E., & Bannert, M. (2018). Differential effects of students’ self-directed metacognitive prompts. Computers in Human Behavior, 86, 165–173.Google Scholar
Rey, G. D., Beege, M., Nebel, S., Wirzberger, M., Schmitt, T. H., & Schneider, S. (2019). A meta-analysis of the segmenting effect. Educational Psychology Review, 31, 389–419.Google Scholar
Rouet, J.-F., Levonen, J. J., Dillon, A., & Spiro, R. J. (eds.) (1996). Hypertext and Cognition. Mahwah, NJ: Erlbaum.Google Scholar
Salmerón, L., Cañas, J. J., Kintsch, W., & Fajardo, I. (2005). Reading strategies and hypertext comprehension. Discourse Processes, 40, 171–191.Google Scholar
Scheiter, K., & Gerjets, P. (2007). Learner control in hypermedia environments. Educational Psychology Review, 19, 285–307.Google Scholar
Scheiter, K., Gerjets, P., Vollmann, B., & Catrambone, R. (2009). The impact of learner characteristics on information utilization strategies, cognitive load experienced, and performance in hypermedia learning. Learning and Instruction, 19, 387–401.Google Scholar
Schnackenberg, H. L., Sullivan, H. J., Leader, L. F., & Jones, E. E. K. (1998). Learner preferences and achievement under differing amounts of learner practice. Educational Technology Research and Development, 46, 5–16.Google Scholar
Schwartz, N. H., Andersen, C., Hong, N., Howard, B., & McGee, S. (2004). The influence of metacognitive skills on learners’ memory of information in a hypermedia environment. Journal of Educational Computing Research, 31, 77–93.Google Scholar
Shapiro, A. (2008). Hypermedia design as learner scaffolding. Educational Technology Research and Development, 56, 29–44.Google Scholar
Shapiro, A. M., & Niederhauser, D. S. (2004). Learning from hypertext: Research issues and findings. In Jonassen, D. H. (ed.), Handbook of Research for Educational Communications and Technology (pp. 605–622). Mahwah, NJ: Lawrence Erlbaum.Google Scholar
Sonnenberg, C., & Bannert, M. (2019). Using process mining to examine the sustainability of instructional support: How stable are the effects of metacognitive prompting on self-regulatory behavior? Computers in Human Behavior, 96, 259–272.Google Scholar
Sorgenfrei, C., & Smolnik, S. (2016). The effectiveness of E-learning systems: A review of the empirical literature on learner control. Decision Sciences, 14, 154–184.Google Scholar
Spiro, R. J., & Jehng, J.-C. (1990). Cognitive flexibility and hypertext: Theory and technology for the nonlinear and multidimensional traversal of complex subject matter. In Nix, D., & Spiro, R. J. (eds.), Cognition, Education, and Multimedia (pp. 163–205). Hillsdale, NJ: Lawrence Erlbaum.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
Sweller, J. (2005). Implications of cognitive load theory for multimedia learning. In Mayer, R. E. (ed.), The Cambridge Handbook of Multimedia Learning (pp. 19–30). New York: Cambridge University Press.Google Scholar
Tempelaar, D. T., Rienties, B., & Nguyen, Q. (2020). Individual differences in the preference for worked examples: Lessons from an application of dispositional learning analytics. Applied Cognitive Psychology, 34, 890–905.Google Scholar
Thiede, K. W., Anderson, M. C. M., & Therriault, D. (2003). Accuracy of metacognitive monitoring affects learning of texts. Journal of Educational Psychology, 95, 66–73.CrossRefGoogle Scholar
Thillmann, H., Künsting, J., Wirth, J., & Leutner, D. (2009). Is it merely a question of “what” to prompt or also “when” to prompt? Zeitschrift für Pädagogische Psychologie, 23, 105–115.Google Scholar
Valcke, M. (2002). Cognitive load: Updating the theory? Learning and Instruction, 12, 147–154.Google Scholar
Veenman, M. V. J., van Hout-Wolters, B. H. A. M., & Afflerbach, P. (2006). Metacognition and learning: Conceptual and methodological considerations. Metacognition and Learning, 1, 3–14.Google Scholar
Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated learning. In Hacker, D. J., Dunlosky, J., & Graesser, A. C. (eds.), Metacognition in Educational Theory and Practice (pp. 277–306). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
Winne, P. H., & Nesbit, J. C. (2009). Supporting self-regulated learning with cognitive tools. In Hacker, D. J., Dunlosky, J., & Graesser, A. C. (eds.), Handbook of Metacognition in Education (pp. 259–277). New York: Routledge.Google Scholar
Zhu, E. (1999). Hypermedia interface design: The effects of number of links and granularity of nodes. Journal of Educational Multimedia and Hypermedia, 8, 331–358.Google Scholar
Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into Practice, 41, 64–70.Google Scholar
Zottmann, J. M., Goeze, A., Frank, C., Zentner, U., Fischer, F., & Schrader, J. (2012). Fostering the analytical competency of pre-service teachers in a computer-supported case-based learning environment: A matter of perspective? Interactive Learning Environments, 20, 513–532.Google Scholar
- 2
- Cited by