Annals of Biomedical Engineering

, Volume 35, Issue 8, pp 1312–1323 | Cite as

Comparison of Student Learning in Challenge-based and Traditional Instruction in Biomedical Engineering

  • Taylor MartinEmail author
  • Stephanie D. Rivale
  • Kenneth R. Diller


This paper presents the results of a study comparing student learning in an inquiry-based and a traditional course in biotransport. Collaborating learning scientists and biomedical engineers designed and implemented an inquiry-based method of instruction that followed learning principles presented in the National Research Council report “How People Learn” (HPL). In this study, the intervention group was taught a core biomedical engineering course in biotransport following the HPL method. The control group was taught by traditional didactic lecture methods. A primary objective of the study was to identify instructional methods that facilitate the early development of adaptive expertise (AE). AE requires a combination of two types of engineering skills: subject knowledge and the ability to think innovatively in new contexts. Therefore, student learning in biotransport was measured in two dimensions: A pre and posttest measured knowledge acquisition in the domain and development of innovative problem-solving abilities. HPL and traditional students’ test scores were compared. Results show that HPL and traditional students made equivalent knowledge gains, but that HPL students demonstrated significantly greater improvement in innovative thinking abilities. We discuss these results in terms of their implications for improving undergraduate engineering education.


Adaptive expertise How people learn Biotransport instruction Challenge-based learning Teaching methods Student learning measurements 



The authors gratefully acknowledge the support of the National Science Foundation for the VaNTH Engineering Research Center in Bioengineering Educational Technologies Award Number EEC-9876363. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. The following bioengineering and learning science colleagues made substantial contributions to this study. Robert Roselli and Kevin Seale from Vanderbilt University and Neil Wright from Michigan State University contributed via collaborations in gathering and sharing instructional data from biotransport courses they taught. Sean Brophy from Purdue University contributed via discussions concerning learning science aspects of the research. Robert Roselli also collaborated over a period of years in creating and sharing many biotransport modules and in developing methods for using the modules to teach in the HPL framework.


  1. 1.
    Accreditation Board for Engineering and Technology. Criteria for Accrediting Engineering Programs, 2006–2007. Baltimore, MD: Accreditation Board for Engineering and Technology, 2005Google Scholar
  2. 2.
    Albanese M. A., S. Mitchell (1993) Problem-based learning: a review of literature on its outcomes and implementation issues. Acad. Med. 68:52–81PubMedCrossRefGoogle Scholar
  3. 3.
    Anderson J. R. (1982) Acquisition of a cognitive skill. Psych. Rev. 89:369–406CrossRefGoogle Scholar
  4. 4.
    Augustine, N. (chair). Rising Above the Gathering Storm: Energizing and Employing America for a Brighter Economic Future. Washington, DC: National Academy of Science, National Academy of Engineering, Institute of Medicine, National Academy Press, 2005Google Scholar
  5. 5.
    Barron B. J., D. L. Schwartz, N. J. Vye, A. Moore, A. J. Petrosino, L. Zech, et al. (1998) Doing with understanding: lessons from research on problem- and project-based learning. J. Learn. Sci. 7:271–312Google Scholar
  6. 6.
    Barrows H. S. (1996) Problem-based learning in medicine and beyond: a brief overview. New Dir. Teach. Learn. 68:3–11CrossRefGoogle Scholar
  7. 7.
    Bransford J. D., A. L. Brown, R. R. Cocking (eds) (2000) How People Learn: Mind, Brain, Experience, and School. National Academy Press, Washington, DC, 319 ppGoogle Scholar
  8. 8.
    Brown A. L., J. C. Campione (1996) Psychological theory and the design of innovative learning environments: on procedures, principles and systems. In: Schauble L., Glaser R. (eds) Innovations in Learning: New Environments for Education. Erlbaum, New Jersey, pp. 289–326Google Scholar
  9. 9.
    Carlson M. P., I. Bloom (2005) The cyclic nature of problem solving: an emergent multidimensional problem-solving framework. Educ. Stud. Math. 58:45–75CrossRefGoogle Scholar
  10. 10.
    Chi M. T. H., P. Feltovich, R. Glaser (1981) Categorization and representation of physics problems by experts and novices. Cognit. Sci. 5:121–152CrossRefGoogle Scholar
  11. 11.
    Clough, G. (chair). Educating the Engineer of 2020: Adapting Engineering Education to the New Century. Washington, DC: National Academy of Engineering, National Academy Press, 2005Google Scholar
  12. 12.
    Clough M. P., K. J. Kaufmann (1999) Improving engineering education: a research-based framework for teaching. J. Eng. Ed. 88:527–534Google Scholar
  13. 13.
    de Jong T. (2006) Computer simulations: technological advances in inquiry learning. Science. 312:532–533PubMedCrossRefGoogle Scholar
  14. 14.
    Diller, K. R., R. R. Roselli, and T. Martin. Teaching biotransport based on How People Learn motivated methodology. In: Proceedings of 2004 American Society of Mechanical Engineers International Mechanical Engineering Congress, Anaheim, CA, 2004Google Scholar
  15. 15.
    Dochy F., M. Segersb, P. Van den Bosscheb, D. Gijbels (2003) Effects of problem-based learning: a meta-analysis. Learn Instruct. 13:533–568CrossRefGoogle Scholar
  16. 16.
    Fisher, F. F., and P. Peterson. A tool to measure adaptive expertise in biomedical engineering students. In: Proceedings of the 2001 American Society for Engineering Education Annual Conference, Albuquerque, NM, 2001Google Scholar
  17. 17.
    Harris T. R., J. D. Bransford, S. P. Brophy (2002) Roles for the learning sciences and learning technologies in biomedical engineering education: a review of recent advances. Annu. Rev. Biomed. Eng. 4:29–48PubMedCrossRefGoogle Scholar
  18. 18.
    Hatano G., K. Inagaki (1986) Two courses of expertise. In: Stevenson H., Azuma J., Hakuta K. (eds) Child Development and Education in Japan. W. H. Freeman & Co., New York, pp. 262–272Google Scholar
  19. 19.
    Lesgold A. M., H. Rubinson, P. Feltovich, R. Glaser, D. Klopfer, Y. Wang (1988) Expertise in a complex skill: diagnosing x-ray pictures. In: Chi M. T. H., Glaser R., Farr M. J. (eds) The Nature of Expertise. Erlbaum, New Jersey, pp. 311–342Google Scholar
  20. 20.
    Lin X., D. L. Schwartz, G. Hatano (2005) Towards teacher’s adaptive metacognition. Educ. Psychol. 40:245–256CrossRefGoogle Scholar
  21. 21.
    Martin, T., A. J. Petrosino, S. R. Rivale, and K. R. Diller. The development of adaptive expertise in biotransport. New Dir. Teach. Learn. (in press), 2006Google Scholar
  22. 22.
    Martin, T., J. Pierson, S. R. Rivale, N. J. Vye, J. D. Bransford, and K. Diller. The function of generating ideas in the Legacy Cycle. In: Innovations 2007: World Innovations in Engineering and Research, edited by W. Aung. Arlington, VA: iNEER, 2007 (manuscript accepted)Google Scholar
  23. 23.
    Martin T., K. Rayne, N. J. Kemp, J. Hart, K. R. Diller (2005) Teaching for adaptive expertise in biomedical engineering ethics. Sci. Eng. Ethics. 11:257–276PubMedCrossRefGoogle Scholar
  24. 24.
    Pandy M. G., A. J. Petrosino, B. A. Austin, R. E. Barr (2004) Assessing adaptive expertise in undergraduate biomechanics. J. Eng. Educ. 93:211–222Google Scholar
  25. 25.
    Prince M. J., R. M. Felder (2006) Inductive teaching and learning methods: definitions, comparisons, and research bases. J. Eng. Educ. 95:123–138Google Scholar
  26. 26.
    Raufaste E., H. Eyrolle, C. Marine (1998) Pertinence generation in radiological diagnosis: spreading activation and the nature of expertise. Cognit. Sci. 22:517–548CrossRefGoogle Scholar
  27. 27.
    Rayne K., T. Martin, S. P. Brophy, N. J. Kemp, J. Hart, K. R. Diller (2006) The development of adaptive expertise in biomedical engineering ethics. J. Eng. Educ. 95:165–173Google Scholar
  28. 28.
    Roselli R. J., S. P. Brophy (2006) Experiences with formative assessment in engineering classrooms. J. Eng. Educ. 95:325–333Google Scholar
  29. 29.
    Sadler D. R. (1989) Formative assessment and the design of instructional systems. Instruct. Sci. 18:119–144CrossRefGoogle Scholar
  30. 30.
    Schoenfeld A. H. (1989) Explorations of students’ mathematical beliefs and behavior. J. Res. Math. Educ. 20:338–355CrossRefGoogle Scholar
  31. 31.
    Schunn C. D., J. R. Anderson (1999) The generality/specificity of expertise in scientific reasoning. Cognit. Sci. 23:337–370CrossRefGoogle Scholar
  32. 32.
    Schwartz D. L., J. D. Bransford (1998) A time for telling. Cognit. Instruct. 16:475–522CrossRefGoogle Scholar
  33. 33.
    Schwartz D. L., J. D. Bransford, D. Sears (2005) Innovation and efficiency in learning and transfer. In: Mestre J. (ed.) Transfer of Learning from a Modern Multidisciplinary Perspective. Erlbaum, New Jersey, pp. 1–51Google Scholar
  34. 34.
    Schwartz D. L., S. Brophy, X. Lin, J. D. Bransford (1999) Software for managing complex learning: examples from an educational psychology course. Educ. Tech. Res. Dev. 47:39–59CrossRefGoogle Scholar
  35. 35.
    Schwartz D. L., T. Martin (2004) Inventing to prepare for future learning: the hidden efficiency of encouraging original student production in statistics instruction. Cognit. Instruct. 22:129–184CrossRefGoogle Scholar
  36. 36.
    Terezini P. T., A. F. Cabrera, C. L. Colbeck, S. A. Bjorklund (2001) Collaborative learning vs. lecture/discussion: students’ reported learning gains. J. Eng. Educ. 90:123–129Google Scholar
  37. 37.
    Vye N. J., D. L. Schwartz, J. D. Bransford, B. B. Barron, L. Zech (1998) SMART environments that support monitoring, reflection, and revision. In: D. Hacker, J. Dunlosky, A. Graesser (eds) Metacognition in Educational Theory and Practice. Erlbaum, New Jersey, pp. 305–346Google Scholar
  38. 38.
    Walker, J. M. T., S. P. Brophy, L. L. Hodge, and J. D. Bransford (2007) Establishing experiences to develop a wisdom of professional practice. New Dir. Teach. Learn. 108:49–58 Google Scholar
  39. 39.
    White B. Y., J. R. Fredrickson (1998) Inquiry, modeling, and metacognition: making science accessible to all students. Cognit. Instruct. 16:3–118CrossRefGoogle Scholar
  40. 40.
    Wineburg S. (1998) Reading Abraham Lincoln: an expert/expert study in interpretation of historical texts. Cognit. Sci. 22:319–346CrossRefGoogle Scholar

Copyright information

© Biomedical Engineering Society 2007

Authors and Affiliations

  • Taylor Martin
    • 1
    Email author
  • Stephanie D. Rivale
    • 1
  • Kenneth R. Diller
    • 2
  1. 1.Department of Curriculum and InstructionThe University of Texas at AustinAustinUSA
  2. 2.Department of Biomedical EngineeringThe University of Texas at AustinAustinUSA

Personalised recommendations