Advances in Specifying What Is to Be Learned: Reflections on the Themes in Chapters 6–8

doi:10.1017/CBO9780511609817.011

9 - Advances in Specifying What Is to Be Learned: Reflections on the Themes in Chapters 6–8

Published online by Cambridge University Press: 04 August 2010

Richard E. Mayer

Edited by

K. Anders Ericsson

Show author details

K. Anders Ericsson: Affiliation:
Florida State University

Book contents

Get access

Summary

INTRODUCTION

Advances in Instructional Design

If you want to be successful in designing effective instruction, you need to begin with a clear specification of what is to be learned. This is a central premise underlying various instructional-design theories since their inception (Reigeluth, 1983, 1999; Reiser & Dempsey, 2007). The three chapters in this section provide a useful description of advances in the field of instructional design, focusing on techniques for specifying what is to be learned. In this review, after a brief analysis of the types of knowledge, I examine four phases in the evolution of instructional-design approaches. As summarized in Table 9.1, these phases involve different ways of characterizing what is to be learned – compartmentalized behaviors, compartmentalized knowledge, integrated knowledge, and individualized knowledge.

APPLYING THE SCIENCE OF INSTRUCTION

Learning is a change in the learner's knowledge due to experience (Mayer, 2001). Instruction is the systematic manipulation of the learner's experience in order to foster learning (Mayer, 2003). In short, learning involves a change in the learner's knowledge and the goal of instruction is to foster a change in the learner's knowledge. However, you can only infer a change in the learner's knowledge indirectly by observing a change in the learner's behavior, such as changes in the learner's performance on a specified task.

Type: Chapter
Information: Development of Professional Expertise
Toward Measurement of Expert Performance and Design of Optimal Learning Environments
, pp. 203 - 212

DOI: https://doi.org/10.1017/CBO9780511609817.011 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2009

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

Anderson, L. W., Krathwohl, D. R., Airasian, P. W., Cruikshank, K. A., Mayer, R. E., Pintrich, P., et al. (2001). Taxonomy for learning, teaching, and assessing: A revision of Bloom's taxonomy of educational objectives. New York: Longman.Google Scholar

Blascovich, J. J., & Hartel, C. R. (Eds.). (2008). Human behavior in military contexts. Washington, DC: National Academy Press.

Bratton-Jeffery, M. F., Hoffman, S. Q., & Jeffery, A. B. (2007). Instructional design opportunities in military education and training environments. In Reiser, R. A. & Dempsey, J. V. (Eds.), Trends and issues in instructional design and technology (2nd ed., pp. 185–196). Upper Saddle River, NJ: Pearson Merrill Prentice-Hall.Google Scholar

Brown, J. S., & Burton, R. R. (1978). Diagnostic models for procedural bugs in basic mathematical skills. Cognitive Science, 2, 155–192.CrossRef Google Scholar

Clark, R. E. (Ed.). (2001). Learning from media. Greenwich, CT: Information Age Publishing.

Ericsson, K. A. (2006). The influence of experience and deliberate practice on the development of superior expert performance. In Ericsson, K. A., Charness, N., Feltovich, P. J., & Hoffman, R. R. (Eds.), The Cambridge handbook of expertise and expert performance (pp. 683–703). New York: Cambridge University Press.CrossRef Google Scholar

Gagné, R. M. (1965). The conditions of learning. New York: Holt, Rinehart, and Winston.Google Scholar

Gagné, R. M. (1968). Learning hierarchies. Educational Psychologist, 6, 1–9.CrossRef

Jonassen, D. H., Tessmer, M., & Hannum, W. H. (1999). Task analysis methods for instructional design. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar

Jonassen, D. H., Lee, C. B., Yang, C. C., & Laffey, J. (2005). The collaboration principle in multimedia learning. In Mayer, R. E. (Ed.), The Cambridge handbook of multimedia learning (pp. 215–228). New York: Cambridge University Press.Google Scholar

Kolodner, J. L., Crismond, D., Frase, B. B., Gray, J. T., Holbrook, J., Ryan, M., et al. (2003). Problem-based learning meets case-based reasoning in the middle-school science classroom: Putting a learning-by-design curriculum into practice. Journal of the Learning Sciences, 12, 495–548.CrossRef Google Scholar

Mager, R. F. (1962). Preparing instructional objectives. Belmont, CA: Fearon Publishers.Google Scholar

Mayer, R. E. (Ed.). (1988). Learning and teaching computer programming. Mahwah, NJ: Lawrence Erlbaum Associates.

Mayer, R. E. (Ed.). (2001). Multimedia learning. New York: Cambridge University Press.CrossRef

Mayer, R. E. (Ed.). (2003). Learning and instruction. Upper Saddle River, NJ: Pearson Merrill Prentice-Hall.

Mayer, R. E. (Ed.). (2004). Should there be a three strikes rule against pure discovery learning? The case for guided methods of instruction. American Psychologist, 59(1), 14–19.CrossRef

Mayer, R. E., Almeroth, K., Bimber, B., Chun, D., Knight, A., & Campbell, A. (2006). Technology comes to college: Understanding the cognitive consequences of infusing technology in college classrooms. Educational Technology, 46(2), 48–53.

Papert, S. (1980). Mindstorms. New York: Basic Books.Google Scholar

Reigeluth, C. M. (Ed.). (1983). Instructional-design theories and models. Hillsdale, NJ: Lawrence Erlbaum Associates.

Reigeluth, C. M. (1999). Instructional-design theories and models: Volume II. Mahwah, NJ: Lawrence Erlbaum Associates.

Reiser, R. A., & Dempsey, J. V. (Ed.). (2007). Trends and issues in instructional design and technology (2nd ed.). Upper Saddle River, NJ: Pearson Merrill Prentice-Hall.