Advertisement

Training for reflective expertise: A four-component instructional design model for complex cognitive skills

  • Jeroen J. G. van Merriënboer
  • Otto Jelsma
  • Fred G. W. C. Paas
Research

Abstract

This article presents a four-component instructional design model for the training of complex cognitive skills. In the analysis phase, the skill is decomposed into a set of recurrent skills that remain consistent over problem situations and a set of nonrecurrent skills that require variable performance over situations. In the design phase, two components relate to the design of practice; they pertain to the conditions under which practice leads either to rule automation during the performance of recurrent skills or to schema acquisition during the performance of nonrecurrent skills. The other two components relate to the design of information presentation; they pertain to the presentation of information that supports the performance of either recurrent or nonrecurrent skills. The basic prediction of the model is that its application leads to “reflective expertise” and increased performance on transfer tasks. Applications of the model that support this prediction are briefly discussed for the training of fault management in process industry, computer programming, and statistical analysis.

Keywords

Computer Programming Analysis Phase Educational Technology Variable Performance Design Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Anderson, J. R. (1983).The architecture of cognition. Cambridge, MA: Harvard University Press.Google Scholar
  2. Anderson, J. R. (1987). Skill acquisition: Compilation of weak-method problem solutions.Psychological Review, 94, 192–210.CrossRefGoogle Scholar
  3. Anderson, J. R. (1988). The expert module. In M. C. Polson & J. J. Richardson (Eds.).Foundations of intelligent tutoring systems (pp. 21–53). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
  4. Anderson, J. R., Boyle, C. F., Corbett, A., & Lewis, M. (1986).Cognitive modelling and intelligent tutoring (Tech. Rep. No. ONR-86/1). Pittsburgh, PA: Carnegie-Mellon University, Dept. of Psychology.Google Scholar
  5. Anderson, J. R., & Thompson, R. (1987).Use of analogy in a production system architecture (Tech. Rep.). Pittsburgh, PA: Carnegie Mellon University, Dept. of Psychology.Google Scholar
  6. Annett, J., & Sparrow, J. (1985). Transfer of training: A review of research and practical implications.Programmed Learning and Educational Technology, 22, 116–124.Google Scholar
  7. Brooks, L. W., & Dansereau, D. F. (1987). Transfer of information: An instructional perspective. In S. M. Cormier & J. D. Hagman (Eds.).Transfer of learning: Contemporary research and applications (pp. 121–150). San Diego, CA: Academic Press.Google Scholar
  8. Carbonell, J. G. (1984). Learning by analogy: Formulating and generalizing plans from past experience. In R. S. Michaelsky, J. G. Carbonell, & T. M. Mitchell (Eds.),Machine learning: An artificial intelligence approach (Vol. 1, pp. 137–161). Berlin: Springer-Verlag.Google Scholar
  9. Carbonell, J. G. (1986). Deprivational analogy: A theory of reconstructive problem solving and expertise acquisition. In R. S. Michaelsky, J. G. Carbonell, & T. M. Mitchell (Eds.),Machine learning: An artificial intelligence approach (Vol. 2, pp. 371–392). Los Altos, CA: Morgan Kaufman.Google Scholar
  10. Carroll, J. M., Smith-Kerker, P. L., Ford, J. R., & Mazur-Rimetz, S. A. (1986).The minimal manual (IBM Research Rep. 11637). Yorktown Heights, NY: IBM Watson Research Center.Google Scholar
  11. Carroll, J. M., Smith-Kerker, P. L., Ford, J. R., & Mazur-Rimetz, S. A. (1988). The minimal manual.Human-Computer Interaction, 3, 123–153.Google Scholar
  12. Charney, D. H., & Reder, L. M. (1986).Initial skill learning: An analysis of how elaborations facilitate the three components (Tech. Rep.). Pittsburgh, PA: Carnegie-Mellon University, Dept. of Psychology.Google Scholar
  13. Collins, A., & Stevens, A. L. (1983). A cognitive theory of inquiry teaching. In C. M. Reigeluth (Ed.),Instructional design theories and models (pp. 247–278). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
  14. Cooper, G., & Sweller, J. (1987). Effects of schema acquisition and rule automation on mathematical problem-solving transfer.Journal of Educational Psychology, 79, 347–362.CrossRefGoogle Scholar
  15. Cormier, S. M., & Hagman, J. D. (Eds.). (1987).Transfer of learning: Contemporary research and applications. San Diego, CA: Academic Press.Google Scholar
  16. Fabiani, M., Buckley, J., Gratton, G., Coles, M. G. H., & Donchin, E. (1989). The training of complex task performance.Acta Psychologica, 71, 259–299.CrossRefGoogle Scholar
  17. Fisk, A. D., & Gallini, J. K. (1989). Training consistent components of tasks: Developing an instructional system based on automatic/controlled processing principles.Human Factors, 31, 453–463.Google Scholar
  18. Frederiksen, J. R., & White, B. Y. (1989). An approach to training based upon principled task composition.Acta Psychologica, 71, 89–146.CrossRefGoogle Scholar
  19. Gentner, D., & Stevens, A. L. (1983).Mental models. Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
  20. Gopher, D., Weil, M., & Siegel, D. (1989). Practice under changing priorities: An approach to the training of complex skills.Acta Psychologica, 71, 147–177.CrossRefGoogle Scholar
  21. Gropper, G. L. (1974).Instructional strategies. Englewood Cliffs, NJ: Educational Technology.Google Scholar
  22. Jelsma, O. (1989).Instructional control of transfer. Enschede, The Netherlands: Bijlstra & Van Merriënboer.Google Scholar
  23. Jelsma, O., & Bijlstra, J. P. (1988). Training for transfer in learning to detect, diagnose, and compensate system failures.Proceedings of the Seventh European Annual Conference on Human Decision Making and Manual Control (pp. 256–262), Paris.Google Scholar
  24. Jelsma, O., & Bijlstra, J. P. (1990).Process: Program for Research on Operator Control in an Experimental Simulated Setting.IEEE Transactions on Systems, Man, and Cybernetics, 20, 1221–1228.CrossRefGoogle Scholar
  25. Jelsma, O., Van Merriënboer, J. J. G., & Bijlstra, J. P. (1990). TheAdapt design model: Towards instructional control of transfer.Instructional Science, 19, 89–120.CrossRefGoogle Scholar
  26. Landa, L. N. (1983). The algo-heuristic theory of instruction. In C. M. Reigeluth (Ed.),Instructional design theories and models (pp. 163–211). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
  27. Larkin, J., McDermott, J., Simon, D., & Simon, H. (1980). Models of competence in solving physics problems.Cognitive Science, 4, 317–348.CrossRefGoogle Scholar
  28. Lee, T. D., & Magill, R. A. (1985). Can forgetting facilitate skill acquisition? In D. Goodman, R. B. Wilberg, & I. M. Franks (Eds.),Differing perspectives in motor learning, memory and control (pp. 3–22). Amsterdam, The Netherlands: Elsevier Science Publishers.Google Scholar
  29. Lewis, M. W., & Anderson, J. R. (1985). Discrimination of operator schemata in problem solving: Learning from examples.Cognitive Psychology, 17, 26–65.CrossRefGoogle Scholar
  30. Linn, M. C. (1985). The cognitive consequences of programming instruction in classrooms.Educational Researcher, 14(5), 14–29.Google Scholar
  31. Mayer, R. E., & Greeno, J. G. (1972). Structural differences between learning outcomes produced by different instructional methods.Journal of Educational Psychology, 63, 165–173.Google Scholar
  32. McDaniel, M. A., & Schlager, M. S. (1990). Discovery learning and transfer of problem-solving skill.Cognition and Instruction, 7, 129–159.CrossRefGoogle Scholar
  33. Merrill, M. D. (1983). Component display theory. In C. M. Reigeluth (Ed.),Instructional-design theories and models: An overview of their current status (pp. 278–333). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
  34. Merrill, P. (1987). Job and task analysis. In R. M. Gagné (Ed.),Instructional technology: Foundations (pp. 141–173). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
  35. Mettes, C. T. W., Pilot, A., & Roossink, H. J. (1981). Linking factual knowledge and procedural knowledge in solving science problems: A case study in a thermodynamics course.Instructional Science, 10, 333–361.CrossRefGoogle Scholar
  36. Morris, N. M., & Rouse, W. B. (1985). The effects of type of knowledge upon human problem solving in a process control task.IEEE Transactions on Systems, Man, and Cybernetics, 15, 698–707.Google Scholar
  37. Myers, G. L., & Fisk, A. D. (1987). Training consistent task components: Application of automatic and controlled processing theory to industrial task training.Human Factors, 29, 255–268.Google Scholar
  38. Olsen, S. E., & Rasmussen, J. (1989).The reflective expert and the prenovice: Notes on skill-, rule-, and knowledge-based performance in the setting of instruction and training (Tech. Rep.). Roskilde, Denmark: Risö National Laboratory.Google Scholar
  39. Owen, E., & Sweller, J. (1985). What do students learn while solving mathematics problems?Journal of Educational Psychology, 77, 272–284.CrossRefGoogle Scholar
  40. Paas, F. G. W. C., & Van Merriënboer, J. J. G. (1992). Training voor transfer van statistische vaardigheden: Toepassing van een vier-componenten instructie-ontwerpmodel [Training for transfer of statistical skills: Application of a four-component instructional design model].Tijdschrift voor Onderwijsresearch, 17, 15–25.Google Scholar
  41. Pea, R. D., & Kurland, M. (1984). On the cognitive effects of learning computer programming.New Ideas in Psychology, 2, 131–168.CrossRefGoogle Scholar
  42. Perkins, D. N., & Salomon, G. (1989). Are cognitive skills context-bound?Educational Researcher, 18, 16–25.Google Scholar
  43. Pieters, J. M., Jelsma, O., & Van Merriënboer, J. J. G. (1987, September).Skill acquisition: ADAPT instructional time to desired level of transfer. Paper presented at the Second European Conference for Research on Learning and Instruction (EARLI), Tübingen, Germany.Google Scholar
  44. Polson, P. G., & Kieras, D. E. (1984). A formal description of users' knowledge of how to operate a device and user complexity.Behavior Research, Methods, Instruments, & Computers, 16, 249–255.Google Scholar
  45. Proctor, R. W., & Reeve, T. G. (1988). The acquisition of task-specific productions and modification of declarative representations in spatial-precueing tasks.Journal of Experimental Psychology: General, 117, 182–196.CrossRefGoogle Scholar
  46. Reigeluth, C. M. (Ed.) (1983a).Instructional design theories and models: An overview of their current status. Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
  47. Reigeluth, C. M. (1983b). Meaningfulness and instruction relating what is being learned to what a student knows.Instructional Science, 12, 197–218.CrossRefGoogle Scholar
  48. Reigeluth, C. M., & Merrill, M. D. (1984).Extended task analysis procedures (ETAP): User's manual. Lanham, MD: University Press of America.Google Scholar
  49. Resnick, L. B. (1976). Task analysis in instructional design: Some cases from mathematics. In D. Klahr (Ed.),Cognition and instruction (pp. 51–80). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
  50. Scandura, J. M. (1983). Instructional strategies based on the structural learning theory. In C. M. Reigeluth (Ed.),Instructional design theories and models (pp. 213–246). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
  51. Schank, R. C., & Abelson, R. P. (1977).Scripts, plans, goals, and understanding. Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
  52. Schneider, W. (1985). Training high-performance skills: Fallacies and guidelines.Human Factors, 27, 285–300.Google Scholar
  53. Schneider, W., & Fisk, A. D. (1982). Degree of consistent training: Improvements in search performance and automatic process development.Perceptions & Psychophysics, 31, 160–168.Google Scholar
  54. Schneider, W., & Shiffrin, R. M. (1977). Controlled and automatic human information processing: I. Detection, search, and attention.Psychological Review, 84, 1–66.CrossRefGoogle Scholar
  55. Schoenfeld, A. H. (1979). Can heuristics be taught? In J. Lochhead & J. Clement (Eds.),Cognitive process instruction (pp. 315–338). Philadelphia: Franklin Institute Press.Google Scholar
  56. Shea, J. B., & Zimny, S. T. (1983). Context effects in memory and learning movement information. In R. A. Magill (Ed.),Memory and control of action (pp. 345–366). Amsterdam, The Netherlands: Elsevier North-Holland.Google Scholar
  57. Shepherd, A. (1986). Issues in the training of process operators.International Journal of Industrial Ergonomics, 1, 49–64.CrossRefGoogle Scholar
  58. Shepherd, A., Marshall, E. C., Turner, A., & Duncan, K. D. (1977). Diagnosis of plant failures from a control panel: A comparison of three training methods.Ergonomics, 20, 347–361.Google Scholar
  59. Shiffrin, R. M., & Schneider, W. (1977). Controlled and automatic human information processing: II. Perceptual learning, automatic attending, and a general theory.Psychological Review, 84, 127–190.CrossRefGoogle Scholar
  60. Singley, M. K., & Anderson, J. R. (1988). A keystroke analysis of learning and transfer in text editing.Human-Computer Interaction, 3, 223–274.Google Scholar
  61. Singley, M. K., & Anderson, J. R. (Eds.) (1989).The transfer of cognitive skill. Cambridge, MA: Harvard University Press.Google Scholar
  62. Soloway, E. (1985). From problems to programs via plans: The content and structure of knowledge for introductory LISP programming.Journal of Educational Computing Research, 1, 157–172.Google Scholar
  63. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning.Cognitive Science, 12, 257–285.CrossRefGoogle Scholar
  64. Sweller, J. (1989). Cognitive technology: Some procedures for facilitating learning and problem solving in mathematics and science.Journal of Educational Psychology, 4, 457–466.Google Scholar
  65. Sweller, J., Chandler, P., Tierney, P., & Cooper, M. (1990). Cognitive load as a factor in the structuring of technical material.Journal of Experimental Psychology: General, 119, 176–192.CrossRefGoogle Scholar
  66. Sweller, J., Mawer, R., & Ward, M. (1983). Development of expertise in mathematical problem solving.Journal of Experimental Psychology: General, 112, 634–656.Google Scholar
  67. Tarmizi, R. A., & Sweller, J. (1988). Guidance during mathematical problem solving.Journal of Educational Psychology, 80, 424–436.CrossRefGoogle Scholar
  68. Tennyson, R. D., & Cocchiarella, M. J. (1986). An empirically based instructional design theory for teaching concepts.Review of Educational Research, 56, 40–71.Google Scholar
  69. Tennsyon, R. D., & Rasch, M. (1988). Linking cognitive learning theory to instructional prescriptions.Instructional Science, 17, 369–385.Google Scholar
  70. Thorndyke, P. W., & Hayes-Roth, B. (1979). The use of schemata in the acquisition and transfer of knowledge.Cognitive Psychology, 11, 82–106.CrossRefGoogle Scholar
  71. Tromp, T. J. M. (1989).The acquisition of expertise in computer programming skill. Amsterdam, The Netherlands: Thesis Publishers.Google Scholar
  72. van Merriënboer, J. J. G. (1990a). Strategies for programming instruction in high school: Program completion vs. program generation.Journal of Educational Computing Research, 6, 265–287.Google Scholar
  73. van Merriënboer, J. J. G. (1990b).Teaching introductory computer programming: A perspective from instructional technology. Enschede, The Netherlands: Bijlstra & van Merriënboer.Google Scholar
  74. van Merriënboer, J. J. G., & De Croock, M. B. M. (in press). Strategies for computer-based programming instruction: Program completion vs. program generation.Journal of Educational Computing Research.Google Scholar
  75. van Merriënboer, J. J. G., & Krammer, H. P. M. (1987). Instructional strategies and tactics for the design of introductory computer programming courses in high school.Instructional Science, 16, 251–285.Google Scholar
  76. van Merriënboer, J. J. G., & Krammer, H. P. M. (1990). The “completion strategy” in programming instruction: Theoretical and empirical support. In S. Dijkstra, B. H. M. Van Hout-Wolters, & P. C. Van der Sijde (Eds.),Research on instruction (pp. 45–61). Englewood Cliffs, NJ: Educational Technology.Google Scholar
  77. van Merriënboer, J. J. G., & Paas, F. G. W. C. (1989). Automation and schema acquisition in learning elementary computer programming: Implications for the design of practice.Computers in Human Behavior, 6, 273–289.Google Scholar
  78. Wightman, D. C., & Lintern, G. (1985). Part-task training for tracking and manual control.Human Factors, 27, 267–284.Google Scholar

Copyright information

© Association for Educational Communications and Technology 1992

Authors and Affiliations

  • Jeroen J. G. van Merriënboer
    • 1
  • Otto Jelsma
    • 2
  • Fred G. W. C. Paas
    • 1
  1. 1.Department of Instructional Technologythe University of TwenteUSA
  2. 2.the Logistic Training Division of Fokker Aircraft BVthe Netherlands

Personalised recommendations