Skip to main content

Advertisement

Log in

High school student modeling in the engineering design process

  • Published:
International Journal of Technology and Design Education Aims and scope Submit manuscript

Abstract

A diverse group of 20 high school students from four states in the US were individually provided with an engineering design challenge. Students chosen were in capstone engineering courses and had taken multiple engineering courses. As students considered the problem and developed a solution, observational data were recorded and artifacts collected. Quantitative methods were used to identify how students allocated their time across different types of modeling. Qualitative methods were used to review data from three students who spent substantial time engaged in graphical and two kinds of mathematical modeling. These students were profiled and their patterns of modeling are represented visually and described in context. Much of the modeling done by these 20 students was graphical in nature. Few students informed their thinking with mathematical representations, yet predictive mathematical modeling is essential to engineering design. Implications for the classroom include encouraging students to transfer understanding of science and mathematics into technology and engineering contexts through modeling.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  • Atman, C., Chimka, J. R., Bursic, K. M., & Nachtmann, H. L. (1999). A comparison of freshman and senior engineering design processes. Design Studies, 20, 131–152.

    Article  Google Scholar 

  • Atman, C., Adams, R. S., Cardella, M., Turns, J., Mosborg, S., & Saleem, J. (2007). Engineering design processes: A comparison of students and expert practitioners. Journal of Engineering Education, 96(4), 359–379.

    Article  Google Scholar 

  • Atman, C., Kilgore, D., & McKenna, A. (2008). Characterizing design learning: A mixed-methods study of engineering designers’ use of language. Journal of Engineering Education, 97(3), 309–326.

    Article  Google Scholar 

  • Becker, K., Mentzer, N., & Park, K. (2012). High school student engineering design thinking and performance. Paper presented at the ASEE 2012 Annual Conference and Exposition, San Diego: California.

  • Bransford, J. D., Brown, A. L., & Cocking, R. R. (Eds.). (2000). How people learn: Brain, mind, experience, and school: Expanded edition. Washington, DC: National Academy Press.

    Google Scholar 

  • Brophy, S., Klein, S., Portsmore, M., & Rogers, C. (2008). Advancing engineering education in P-12 classrooms. Journal of Engineering Education, 97(3), 369–387.

    Article  Google Scholar 

  • Bucciarelli, L. L. (1988). An ethnographic perspective on engineering design. Design Studies, 9(3), 159–168.

    Article  Google Scholar 

  • Bursic, K. M., & Atman, C. (1997). Information gathering: a critical step for quality in the design process. Quality Management Journal, 4(4), 60–75.

    Google Scholar 

  • Chamberlin, S. A., & Moon, S. M. (2005). Model-eliciting activities as a tool to develop and identify creatively gifted mathematicians. The Journal of Secondary Gifted Education, XVII(1), 37–47.

    Google Scholar 

  • Christiaans, H., & Dorst, K. (1992). Cognitive models in industrial design engineering: A protocol study. Design Theory and Methodology, 42, 131–140.

    Google Scholar 

  • Collins, A., Brown, J., & Holum, A. (1991). Cognitive apprenticeship: Making thinking visible. American Educator, 6, 38–46.

    Google Scholar 

  • Committee on Prospering in the Global Economy of the 21st Century & Committee on Science Engineering and Public Policy. (2007). Rising above the gathering storm: Energizing and employing america for a brighter economic future (pp. 1–564). Washington, DC:National Academies Press.

  • Creswell, J. W. (2007). Qualitative inquiry and research design (2nd ed.). Thousand Oaks: Sage Publications.

    Google Scholar 

  • Diefes-dux, H., Moore, T., Zawojewski, J., Imbrie, P. K., & Follman, D. (2004). A framework for posing open-ended engineering problems: model-eliciting activities. Paper presented at the 34th Annual Frontiers in Education.

  • Diefes-dux, H., Hjalmarson, M. A., Miller, T. K., & Lesh, R. (2008). Model-eliciting for engineering education. In J. Zawojewski, H. Diefes-Dux, & K. Bowman (Eds.), Models and modeling in engineering education: Designing experiences for all students (pp. 17–35). Rotterdam, the Netherlands: Sense Publishers.

    Google Scholar 

  • Doerr, H. M., & English, L. D. (2003). A Modeling perspective on students’ mathematical reasoning about data. Journal for Research in Mathematics Education, 34(2), 110–136.

    Article  Google Scholar 

  • Dym, C. L., & Little, P. (2008). Engineering design: A project based approach (3rd ed.). Hoboken: Wiley.

    Google Scholar 

  • Dym, C. L., Agogino, A. M., Eris, O., Frey, D. D., & Leifer, L. J. (2005). Engineering design thinking, teaching, and learning. Journal of Engineering Education, 34(1), 103–120.

    Article  Google Scholar 

  • English, L. D. (2008). Mathematical modeling: Linking mathematics, science and arts in the primary curriculum. Paper presented at the 2nd International Symposium on Mathematics and its Connections to the Arts and Sciences Odense, Denmark.

  • English, L. D. (2010). Modeling with complex data in the primary school. In R. Lesh, P. L. Galbraith, C. R. Haines, & A. Hurford (Eds.), Modeling students’ mathematical modeling competencies (pp. 287–299). Boston, MA: Springer.

    Chapter  Google Scholar 

  • English, L. D., & Mousoulides, N. G. (2011). Engineering-based modelling experiences in the elementary and middle classroom. In M. S. Khine & I. M. Saleh (Eds.), Models and Modeling (pp. 173–194). Boston: Springer.

    Chapter  Google Scholar 

  • Ennis, C. W., & Gyeszly, S. W. (1991). Protocol analysis of the engineering systems design process. Research in Engineering Design, 3(1), 15–22.

    Article  Google Scholar 

  • Ericsson, K., & Simon, H. (1993). Protocol analysis: verbal reports as data (revised version). Cambridge: MIT Press.

    Google Scholar 

  • France, B., Compton, V. J., & Gilbert, J. K. (2010). Understanding modelling in technology and science: the potential of stories from the field. International Journal of Technology and Design Education, 21(3), 381–394. doi:10.1007/s10798-010-9126-4.

    Article  Google Scholar 

  • Gainsburg, J. (2006). The mathematical modeling of structural engineers. Mathematical Thinking and Learning, 8(1), 3–36.

    Article  Google Scholar 

  • Garmire, E., & Pearson, G. (Eds.). (2006). Tech tally: Approaches to assessing technological literacy. Washington, DC: National Academies Press.

    Google Scholar 

  • Guindon, R. (1990). Designing the design process: Exploiting opportunistic thoughts. Human-Computer Interaction, 5, 305–344.

    Article  Google Scholar 

  • Hacker, M., & Burghardt, D. (2008). Addressing issues related to technology and engineering. The Technology Teacher, 68(3), 28–33.

    Google Scholar 

  • Hruschka, D., Schwartz, D., St.John, D., Picone-Decaro, E., Jenkins, R., & Carey, J. (2004). Reliability in coding open-ended data: Lessons learned from HIV behavioral research. Field Methods, 16(3), 307–331. doi:10.1177/1525822X04266540.

    Article  Google Scholar 

  • Huffman, T., Mentzer, N., & Becker, K. (2013). High school students modeling behaviors during engineering design. Paper presented at the ASEE 2013 Annual Conference and Exposition, Atlanta, GA.

  • International Technology Education Association. (2007). Standards for technological literacy: Content for the study of technology (Vol. 82). Reston, VA: International Technology Association.

    Google Scholar 

  • James, C. M., Goldman, S. R., & Vandermolen, H. (1994). The Role of planning in simple digital circuit design. Paper presented at the American Educational Research Association Conference, New Orleans, LA.

  • Johnson-Laird, P. N., Girotto, V., & Legrenzi, P. (1998). Mental models: A gentle guide for outsiders. Sistemi Intelligenti, 9, 33–68.

    Google Scholar 

  • Katehi, L., Pearson, G., & Feder, M. (Eds.). (2009). Engineering in K-12 education. Washington, DC: The National Academies Press.

    Google Scholar 

  • Lehrer, R., & Schauble, L. (2000). Developing model-based reasoning in mathematics and science. Journal of Applied Developmental Psychology, 21(1), 39–48. doi:10.1016/S0193-3973(99)00049-0.

    Article  Google Scholar 

  • Lesh, R., & Doerr, H. M. (Eds.). (2003). Beyond constructivism: Models and modeling perspectives on mathematics problem solving, learning, and teaching. Mahwah, NJ: Lawrence Erlbaum Associates Inc.

    Google Scholar 

  • Lesh, R., Hoover, M., Hole, B., Kelly, A., & Post, T. (2000). Principles for developing thought-revealing activities for students and teachers. In A. K. R. Lesh (Ed.), Handbook of research design in mathematics and science education (pp. 591–646). Mahwah, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Mancosu, P. (2011). Explanation in mathematics. In E. N. Zalta (Ed.), The standford encyclopedia of philosophy (Summer 2011 ed.). Stanford, CA: The Metaphysics Research Lab Center for the Study of Language and Information Stanford University. http://plato.stanford.edu/archives/sum2011/entries/mathematics-explanation/.

  • Mawson, B. (2003). Beyond `The Design Process’: An alternative pedagogy for technology education. International Journal of Technology and Design Education, 13(2), 117–128.

    Article  Google Scholar 

  • McKeachie, W. J. (2006). McKeachie’s teaching tips: Strategies, research, and theory for college and university teachers (12th ed.). Boston: Houghton Mifflin.

    Google Scholar 

  • Mentzer, N., Becker, K., & Park, K. (2011). High school students as novice designers. Paper presented at the American Society of Engineering Education, Vancouver, CA.

  • Moore, T. (2006). Student team functioning and the effect on mathematical problem solving in a first-year engineering course. West Lafayette, IN: Purdue University.

    Google Scholar 

  • Moore, T. (2008). Model-eliciting activities: A case-based approach for getting students interested in material science and engineering. Journal of Materials Education, 30(5–6), 295–310.

    Google Scholar 

  • Mosborg, S., Cardella, M., Saleem, J., Atman, C., Adams, R. S., & Turns, J. (2006). Engineering Design Expertise Study, CELT Technical Report CELT-06-01. Seattle: University of Washington.

  • Moussavi, M. (1998). Mathematical modeling in engineering education. Paper presented at the Frontiers in Education Conference, Tempe, AZ.

  • National Academy of Engineering. (2004). The Engineer of 2020. Washington, DC: The National Academies Press.

    Google Scholar 

  • National Academy of Engineering. (2005). Educating the engineer of 2020: Adapting engineering education to the new century. Washington, D.C.: The National Academies Press.

    Google Scholar 

  • Quarteroni, A. (2009). Mathematical models in science and engineering. Simulation, 56(1), 10–19.

    Google Scholar 

  • Ritz, J. M., & Martin, G. (2012). Research needs for technology education: an international perspective. International Journal of Technology and Design Education,. doi:10.1007/s10798-012-9215-7.

    Google Scholar 

  • Rowland, G. (1992). What do instructional designers actually do? Performance Improvement Quarterly, 5(2), 65–86.

    Article  Google Scholar 

  • Schulz, N. N. (1991). Methods to stimulate electrical engineering concepts to non-EE students. Paper presented at the IEEE Proceedings of Southeastcon, Williamsburg, VA.

  • Schwartz, D. L., Bransford, J. D., & Sears, D. L. (2005). Efficiency and innovation in transfer. In J. Mestre (Ed.), Transfer of learning from a modern multidisciplinary perspective. Charlotte: Information Age Publishing.

    Google Scholar 

  • Showalter, Q. (2009). The effect of model-eliciting activities on problem solving process and student disposition toward mathematics. ProQuest dissertations and theses. University of Kansas Retrieved from http://search.proquest.com/docview/288191294?accountid=13360. (288191294).

  • Sutcliffe, A. G., & Maiden, N. A. M. (1992). Analyzing the novice analyst: Cognitive Models in Software Engineering. International Journal of Man-Machine Studies, 36, 719–740.

    Article  Google Scholar 

  • Yair, G. (2000). Reforming motivation: How the structure of instruction affects students’ learning experiences. British Educational Research Journal, 26(2), 191–210.

    Article  Google Scholar 

  • Yildirim, T. P., Shuman, L., & Besterfield-sacre, M. (2010). Model-eliciting activities: Assessing engineering student problem solving and skill integration processes. International Journal of Engineering Education, 26(4), 831–845.

    Google Scholar 

Download references

Acknowledgments

This material is based upon work supported by the National Science Foundation under Grant No. DRL-0918621. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nathan Mentzer.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Mentzer, N., Huffman, T. & Thayer, H. High school student modeling in the engineering design process. Int J Technol Des Educ 24, 293–316 (2014). https://doi.org/10.1007/s10798-013-9260-x

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10798-013-9260-x

Keywords

Navigation