Skip to main content

Characteristics of a cognitive tool that helps students learn diagnostic problem solving

Abstract

Three related studies replicated and extended previous work (J.A. Danielson et al. (2003), Educational Technology Research and Development, 51(3), 63–81) involving the Diagnostic Pathfinder (dP) (previously Problem List Generator [PLG]), a cognitive tool for learning diagnostic problem solving. In studies 1 and 2, groups of 126 and 113 veterinary students, respectively, used the dP to complete case-based homework; groups of 120 and 199, respectively, used an alternative method. Students in the dP groups scored significantly higher (p = .000 and .003, respectively) on final exams than those in control groups. In the third study, 552 veterinary students responding to a questionnaire indicated that the dP’s gating and data synthesis activities aided learning. The dP’s feedback and requirement of completeness appear to aid learning most.

This is a preview of subscription content, access via your institution.

Fig. 1

References

  1. Bordage, G. (1994). Elaborated knowledge: A key to successful diagnostic thinking. Academic Medicine, 69(11), 883–885.

    Article  Google Scholar 

  2. Bordage, G., & Lemieux, M. (1991). Semantic structures and diagnostic thinking of experts and novices. Academic Medicine, 66(9 Suppl), S70–72.

    Article  Google Scholar 

  3. Bransford, J. D. E., Brown, A. L. E., & Cocking, R. R. E. (2000). how people learn: brain, mind, experience, and school. expanded edition. District of Columbia: National Academies Press, 2102 Constitution Avenue N.W., Washington DC 20055.

  4. Danielson, J. A. (1999). The design, development and evaluation of a web-based tool for helping veterinary students learn how to classify clinical laboratory data. Unpublished doctoral dissertation, Virginia Tech, Blacksburg.

  5. Danielson, J. A., Bender, H. S., Mills, E. M., Vermeer, P. J., & Lockee, B. B. (2003). A Tool for Helping Veterinary Students Learn Diagnostic Problem Solving. Educational Technology Research and Development, 51(3), 63–81.

    Article  Google Scholar 

  6. Jonassen, D. H. (2003). Using Cognitive Tools to Represent Problems. Journal of Research on Technology in Education, 35(3), 362–379.

    Google Scholar 

  7. Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). The Expertise Reversal Effect. Educational Psychologist, 38(1), 23–31.

    Article  Google Scholar 

  8. Kozma, R. B. (1987). The Implications of Cognitive Psychology for computer-based learning Tools. Educational Technology, 40(11), 20–25.

    Google Scholar 

  9. Mayer, R. E. (1976). Comprehension as affected by structure of problem representation. Memory and Cognition, 4(3), 249–255.

    Google Scholar 

  10. McGuinness, C. (1986). Problem Representation: The Effects of Spatial Arrays. Memory and Cognition, 14(3), 270–280.

    Google Scholar 

  11. Novak, J. D. (1990). Concept Maps and Vee Diagrams: Two Metacognitive Tools to Facilitate Meaningful Learning. Instructional Science, 19(1), 29–52.

    Article  Google Scholar 

  12. Paas, F., Renkl, A., & Sweller, J. (2003). Cognitive Load Theory and Instructional Design: Recent Developments [Special issue]. Educational Psychologist, 38(1), 1–4.

    Article  Google Scholar 

  13. Pape, S. J., & Tchoshanov, M. A. (2001). The role of representation(s) in developing mathematical understanding. Theory Into Practice, 40(2), 118–127.

    Article  Google Scholar 

  14. Pedhazur, E. J., & Schmelkin, L. P. (1991). Measurement, design, and analysis: An integrated approach. Hillsdale, New Jersey: Lawrence Erlbaum Associates.

    Google Scholar 

  15. Reeve, J. (2005). Understanding motivation and emotion, (4th ed.). New Jersey: John Wiley & Sons.

    Google Scholar 

  16. Salomon, G. (1988). AI in reverse: Computer tools that turn cognitive. J. Educational Computing Research, 4(2), 123–139.

    Article  Google Scholar 

  17. Smith, P. L., & Ragan, T. J. (1999). Instructional design, (2nd ed.). Upper Saddle River, New Jersey: Merrill.

    Google Scholar 

  18. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285.

    Article  Google Scholar 

  19. van Merriënboer, J. J. G., & Ayres, P. (2005). Research on cognitive load theory and its design implications for E-Learning [Special issue]. ETR&D, 53(3).

  20. van Merriënboer, J. J. G., Kirschner, P. A., & Kester, L. (2003). Taking the load off a learner’s mind: instructional design for complex learning. Educational Psychologist, 38(1), 5–13.

    Article  Google Scholar 

  21. Zhang, J. (1997). The nature of representations in problem solving. Cognitive Science, 21(2), 179–217.

    Article  Google Scholar 

Download references

Acknowledgments

The contents of this article were partially developed under a grant from the Learning Anytime Anywhere Partnerships (LAAP), a program of the Fund for the Improvement of Postsecondary Education (FIPSE), U.S. Department of Education. However, these contents do not necessarily represent the policy of the Department of Education, and you should not assume endorsement by the Federal Government. Elements of this article were reported previously by Danielson, Bender, Mills, Vermeer, and Preast as Helping Learners Gain Diagnostic Problem Solving Skills: Specific Aspects of the Diagnostic Pathfinder Software Tied to Learning Outcomes in the published proceedings of the 2004 AECT national convention.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Jared A. Danielson.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Danielson, J.A., Mills, E.M., Vermeer, P.J. et al. Characteristics of a cognitive tool that helps students learn diagnostic problem solving. Education Tech Research Dev 55, 499–520 (2007). https://doi.org/10.1007/s11423-006-9003-8

Download citation

Keywords

  • Cognitive Load Theory
  • Cognitive Tools
  • Diagnostic Problem Solving
  • Feedback
  • Gating