Book contents
- Frontmatter
- Contents
- Preface
- Contributors
- 1 Introduction to Multimedia Learning
- PART I THEORETICAL FOUNDATIONS
- PART II BASIC PRINCIPLES OF MULTIMEDIA LEARNING
- PART III ADVANCED PRINCIPLES OF MULTIMEDIA LEARNING
- 14 The Guided Discovery Principle in Multimedia Learning
- 15 The Worked-Out Examples Principle in Multimedia Learning
- 16 The Collaboration Principle in Multimedia Learning
- 17 The Self-Explanation Principle in Multimedia Learning
- 18 The Animation and Interactivity Principles in Multimedia Learning
- 19 Navigational Principles in Multimedia Learning
- 20 The Site Map Principle in Multimedia Learning
- 21 Prior Knowledge Principle in Multimedia Learning
- 22 The Cognitive Aging Principle in Multimedia Learning
- PART IV MULTIMEDIA LEARNING IN CONTENT AREAS
- PART V MULTIMEDIA LEARNING IN ADVANCED COMPUTER-BASED CONTEXTS
- Author Index
- Subject Index
- References
14 - The Guided Discovery Principle in Multimedia Learning
Published online by Cambridge University Press: 05 June 2012
By
Edited by
Book contents
- Frontmatter
- Contents
- Preface
- Contributors
- 1 Introduction to Multimedia Learning
- PART I THEORETICAL FOUNDATIONS
- PART II BASIC PRINCIPLES OF MULTIMEDIA LEARNING
- PART III ADVANCED PRINCIPLES OF MULTIMEDIA LEARNING
- 14 The Guided Discovery Principle in Multimedia Learning
- 15 The Worked-Out Examples Principle in Multimedia Learning
- 16 The Collaboration Principle in Multimedia Learning
- 17 The Self-Explanation Principle in Multimedia Learning
- 18 The Animation and Interactivity Principles in Multimedia Learning
- 19 Navigational Principles in Multimedia Learning
- 20 The Site Map Principle in Multimedia Learning
- 21 Prior Knowledge Principle in Multimedia Learning
- 22 The Cognitive Aging Principle in Multimedia Learning
- PART IV MULTIMEDIA LEARNING IN CONTENT AREAS
- PART V MULTIMEDIA LEARNING IN ADVANCED COMPUTER-BASED CONTEXTS
- Author Index
- Subject Index
- References
Summary
Abstract
Inquiry or scientific discovery learning environments are environments in which a domain is not directly offered to learners but in which learners have to induce the domain from experiences or examples. Because this is a difficult task the discovery process needs to be combined with guidance for the learner. The most effective way to provide this guidance is to integrate it in the learning environment. Guidance may be directed at one or more of the discovery learning processes, for example, hypothesis generation or monitoring, or at structuring the overall process. With adequate guidance discovery learning can be an effective learning approach in which mainly “intuitive” or “deep” conceptual knowledge can be acquired. Inquiry learning now finds new directions in collaborative inquiry and modeling environments.
Guided Discovery Learning
In the design of learning environments the emphasis in the learning process is often placed on the learning material or the teacher. In this way instruction that explains principles and rules in a domain to a learner is created. This instructive mode of teaching and learning can be contrasted with an inductive learning mode in which the emphasis in the learning process is with the learner. This scientific discovery (or inquiry) learning is characterized by the induction of principles from experiences and/or examples (Swaak & de Jong, 1996). The learner's knowledge acquisition process progresses by stating rules or hypotheses on the basis of concrete situations and by subsequently testing these hypotheses in new situations.
- Type
- Chapter
- Information
- The Cambridge Handbook of Multimedia Learning , pp. 215 - 228Publisher: Cambridge University PressPrint publication year: 2005
References
(1988). Fidelity in the design of instructional simulations. Journal of Computer-Based Instruction, 15, 40–47Google Scholar
Alessi, S. M. (2000a). Building versus using simulations. In & (Eds.), Integrated and holistic perspectives on learning, instruction and technology (pp. 175–196). Dordrecht, The Netherlands: Kluwer Academic PublishersGoogle Scholar
(2000b). Designing educational support in system-dynamic-based interactive learning environments. Simulation and Gaming, 31, 178–196CrossRefGoogle Scholar
Alessi, S. M. (2000c). Simulation design for training and assessment. In & (Eds.), Aircrew training and assessment (pp. 197–222). Mahwah, NJ: Lawrence Erlbaum AssociatesGoogle Scholar
, & (2004). Effectiveness of multimedia-based instruction that emphasizes molecular representations on students' understanding of chemical change. Journal of Research in Science Teaching, 41, 317–338CrossRefGoogle Scholar
, , & (2004). Does adaptive scaffolding facilitate students' ability to regulate their learning with hypermedia?Contemporary Educational Psychology, 29, 344–370CrossRefGoogle Scholar
, , & (1985). Effectiveness of computer-based education in secondary schools. Journal of Computer Based Instruction, 12, 59–68Google Scholar
, & (1984). On the relationship between task performance and associated verbalizable knowledge. The Quarterly Journal of Experimental Psychology, 36A, 209–231CrossRefGoogle Scholar
, & (1999). All other things being equal: Acquisition and transfer of the control of variables strategy. Child Development, 70, 1098–1120CrossRefGoogle ScholarPubMed
, & (1993). The role of anomalous data in knowledge acquisition: A theoretical framework and implications for science instruction. Review of Educational Research, 63, 1–51CrossRefGoogle Scholar
, & (2004). Helping students revise disruptive experientially supported ideas about thermodynamics: Computer visualisations and tactile models. Journal of Research in Science Teaching, 41, 1–23CrossRefGoogle Scholar
(1984). Assumptions underlying the use of different types of simulations. Simulations & Games, 15, 213–234CrossRefGoogle Scholar
(1969). Methodology, foundations of inference and research in the behavioural sciences. The Hague, The Netherlands: MoutonGoogle Scholar
, , & (1991). Interfaces for instructional use of simulations. Education and Computing, 6, 359–385CrossRefGoogle Scholar
(1991). Learning and instruction with computer simulations. Education and Computing, 6, 217–229CrossRefGoogle Scholar
de Jong, T., Beishuizen, J., Hulshof, C. D., Prins, F., van Rijn, H., van Someren, M., et al. (in press). Determinants of discovery learning. In , , & (Eds.), Cognition, education and communication technology. Mahwah, NJ: Lawrence Erlbaum AssociatesGoogle Scholar
, , & (1993). Coping with complex environments: The effects of overviews and a transparent interface on learning with a computer simulation. International Journal of Man-Machine Studies, 39, 621–639CrossRefGoogle Scholar
de Jong, T., & Njoo, M. (1992). Learning and instruction with computer simulations: Learning processes involved. In , , , & (Eds.), Computer-based learning environments and problem solving (pp. 411–429). Berlin, Germany: Springer-VerlagCrossRefGoogle Scholar
, & (1998). Scientific discovery learning with computer simulations of conceptual domains. Review of Educational Research, 68, 179–202CrossRefGoogle Scholar
, , & (2002). Computer-mediated epistemic dialogue: Explanation and argumentation as vehicles for understanding scientific notions. The Journal of the Learning Sciences, 11, 63–103CrossRefGoogle Scholar
Dunbar, K. (2001). What scientific thinking reveals about the nature of cognition. In & (Eds.), Designing for science: Implications from everyday, classroom, and professional settings (pp. 115–140). Mahwah, NJ: Lawrence Erlbaum AssociatesGoogle Scholar
, , & (1990). Learning scientific reasoning skills in microcomputer-based laboratories. Journal of Research in Science Teaching, 27, 173–191CrossRefGoogle Scholar
, & (in press). The relation between prior knowledge and students' collaborative discovery learning processes. Journal of Research in Science TeachingGoogle Scholar
Gijlers, H., & de Jong, T. (submitted). Sharing and confronting propositions in collaborative scientific discovery learning
, & (1989). Simulation fidelity in training system design. New York: Springer-VerlagCrossRefGoogle Scholar
, , , & (2003). Integrating curriculum, instruction, assessment, and evaluation in a technology-supported genetics learning environment. American Educational Research Journal, 40, 495–538CrossRefGoogle Scholar
Hulshof, C. D. (in press). Log file analysis. In (Ed.), Encyclopedia of social measurement. San Diego, CA: Academic PressGoogle Scholar
Hulshof, C. D., & de Jong, T. (submitted). Using just-in-time information to support discovery learning about geometrical optics in a computer-based simulation
, , & (2002). Computer simulations in the high school: Students' cognitive stages, science process skills and academic achievement in microbiology. International Journal of Science Education, 24, 803–821CrossRefGoogle Scholar
, & (2002). Collaborative inquiry and the construction of explanations in the learning of science. Learning and Instruction, 12, 189–213CrossRefGoogle Scholar
, & (in press). Reasoning from data: How students collect and interpret data in science investigations. Journal of Research in Science TeachingGoogle Scholar
(2003). Supporting inquiry learning by promoting normative understanding of multivariable causality. Journal of Research in Science Teaching, 40, 898–921CrossRefGoogle Scholar
, & (1988). Dual space search during scientific reasoning. Cognitive Science, 12, 1–48CrossRefGoogle Scholar
, & (2004). The equivalence of learning paths in early science instruction: Effects of direct instruction and discovery learning. Psychological Science, 15, 661–668CrossRefGoogle Scholar
, , , & (2000). The development of cognitive skills to support inquiry learning. Cognition and Instruction, 18, 495–523CrossRefGoogle Scholar
, & (1988). Educational simulations, tools, games, and microworlds: Computer-based environments for learning. International Journal of Educational Research, 12, 71–79Google Scholar
, , & (1993). The effect of computer simulations on introductory thermodynamics understanding. Educational Technology, 33, 45–58Google Scholar
(2001). On the cognitive conflict as an instructional strategy for conceptual change: A critical appraisal. Learning and Instruction, 11, 357–380CrossRefGoogle Scholar
Linn, M. C., Davis, E. A., & Bell, P. (2004). Inquiry and technology. In , , & (Eds.), Internet environments for science education (pp. 3–28). Mahwah, NJ: Lawrence Erlbaum AssociatesGoogle Scholar
, , & (2003). The effect of external representation on constructing computer models of complex phenomena. Instructional Science, 31, 395–418CrossRefGoogle Scholar
(1987). Educational psychology, a cognitive approach. Boston: Little, Brown and CompanyGoogle Scholar
(2004). Should there be a three-strikes rule against pure discovery learning?American Psychologist, 59, 14–19CrossRefGoogle ScholarPubMed
, , & (2002). Pictorial aids for learning by doing in a multimedia geology simulation game. Journal of Educational Psychology, 94, 171–185CrossRefGoogle Scholar
, , , & (1995). Tutoring: Guided learning by doing. Cognition and Instruction, 13, 315–372CrossRefGoogle Scholar
, & (1992). Business simulations: Validity and effectiveness. Simulation/Games for Learning, 22, 261–285Google Scholar
(2004). Decreasing cognitive load for novice students: Effects of explanatory versus corrective feedback in discovery-based multimedia. Instructional Science, 32, 99–113CrossRefGoogle Scholar
, & (1993). Exploratory learning with a computer simulation for control theory: Learning processes and instructional support. Journal of Research in Science Teaching, 30, 821–844CrossRefGoogle Scholar
, & (1997). Collaborative discovery in a scientific domain. Cognitive Science, 21, 109–146CrossRefGoogle Scholar
(1986). Mental representation: A dual coding approach. New York: Oxford University PressGoogle Scholar
(2001). Cognition, computers, and synthetic science: Building knowledge and meaning through modelling. Review of Research in Education, 25, 1–37Google Scholar
, , , , , , et al. (2004). A scaffolding design framework for software to support science inquiry. The Journal of the Learning Sciences, 13, 337–387CrossRefGoogle Scholar
, , , & (1992). The effectiveness of games for educational purposes: A review of recent research. Simulation and Gaming, 23, 261–276CrossRefGoogle Scholar
, , & (2003). Supporting scientific discovery learning in a simulation environment. Journal of Computer Assisted Learning, 19, 9–20CrossRefGoogle Scholar
Reiser, B. J. (2002). Why scaffolding should sometimes make tasks more difficult for learners. In (Ed.), Computer support for collaborative learning foundations for a CSCL community (pp. 255–264). Hillsdale, NJ: Lawrence Erlbaum AssociatesGoogle Scholar
Reiser, B. J., Tabak, I., Sandoval, W. A., Smith, B., Steinmuller, F., & Leone, T. J. (2001). BGuILE: Strategic and conceptual scaffolds for scientific inquiry in biology classrooms. In & (Eds.), Cognition and instruction: Twenty five years of progress (pp. 263–305). Mahwah, NJ: Lawrence Erlbaum AssociatesGoogle Scholar
(1991). Animation, incidental learning, and continuing motivation. Journal of Educational Psychology, 83, 318–328CrossRefGoogle Scholar
, & (1995). To teach or not to teach? Comparing the use of computer-based simulations in deductive versus inductive approaches to learning with adults in science. Journal of Educational Computing Research, 14, 359–374CrossRefGoogle Scholar
, , & (in press). Discovery learning, representations, and explanation within a computer-based simulation: finding the right mix. Learning and InstructionGoogle Scholar
(1992). Evaluating simulations and games: An economist's view. Simulations/Games for Learning, 22, 308–325Google Scholar
, & (1993). The development of science process skills in authentic contexts. Journal of Research in Science Teaching, 30, 127–152CrossRefGoogle Scholar
, & (1990). A large-scale evaluation of an intelligent discovery world: Smithtown. Interactive Learning Environments, 1, 51–77CrossRefGoogle Scholar
Slotta, J. (2004). The web-based inquiry science environment (WISE): Scaffolding knowledge integration in the science classroom. In , , & (Eds.), Internet environments for science education (pp. 203–233). Mahwah, NJ: Lawrence Erlbaum AssociatesGoogle Scholar
, & (1996). Measuring intuitive knowledge in science: The development of the what-if test. Studies in Educational Evaluation, 22, 341–362CrossRefGoogle Scholar
, , & (2004). The effects of discovery learning and expository instruction on the acquisition of definitional and intuitive knowledge. Journal of Computer Assisted Learning, 20, 225–234CrossRefGoogle Scholar
(1994). Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction, 4, 295–312CrossRefGoogle Scholar
, & (1999). A comparison of cognitive load associated with discovery learning and worked examples. Journal of Educational Psychology, 91, 334–341CrossRefGoogle Scholar
, , & (2000). Learning probability through the use of a collaborative, inquiry-based simulation environment. Journal of Interactive Learning Research, 11, 51–84Google Scholar
van der Meij, J., & de Jong, T. (2004). Learning with multiple representations: Supporting students' translation between representations in a simulation-based learning environment. Paper presented at the American Educational Research Association, San Diego, CA
, & (1991). Supporting hypothesis generation by learners exploring an interactive computer simulation. Instructional Science, 20, 389–404CrossRefGoogle Scholar
, & (1997). An extended dual search space model of learning with computer simulations. Instructional Science, 25, 307–346CrossRefGoogle Scholar
van Joolingen, W. R., & de Jong, T. (2003). SimQuest: Authoring educational simulations. In , , & (Eds.), Authoring tools for advanced technology educational software: Toward cost-effective production of adaptive, interactive, and intelligent educational software (pp. 1–31). Dordrecht, The Netherlands: Kluwer Academic PublishersGoogle Scholar
, , , , & (submitted). Co-Lab: Research and development of an on-line learning environment for collaborative scientific discovery learning. Computers in Human BehaviorGoogle Scholar
, , & (2000). Promoting self directed learning in simulation based discovery learning environments through intelligent support. Interactive Learning Environments, 8, 229–255CrossRefGoogle Scholar
Veermans, K. H., van Joolingen, W. R., & de Jong, T. (submitted). Using heuristics to facilitate discovery learning in a simulation learning environment in a physics domain
, & (1990). Causal model progressions as a foundation for intelligent learning environments. Artificial Intelligence, 42, 99–157CrossRefGoogle Scholar
, , , & (2004). Triple scheme of learning support design for scientific discovery learning based on computer simulation: Experimental research. Journal of Computer Assisted Learning, 20, 269–282CrossRefGoogle Scholar
- 41
- Cited by