Advancing Children’s Engineering Through Desktop Manufacturing

  • Glen Bull
  • Jennifer Chiu
  • Robert Berry
  • Hod Lipson
  • Charles Xie
Chapter

Abstract

Children’s engineering involves design of a solution under specified constraints in response to a particular need or goal. Desktop manufacturing systems enable students to engineer complex solutions with tangible products, expanding the range of possible approaches to engineering education. Desktop manufacturing technologies encompass digital fabrication systems such as 3D printers and computer-controlled die cutting systems and related technologies such as 3D scanners. These systems offer an entry point for advancing children’s engineering as well as connecting to other STEM subjects.

Because desktop manufacturing systems have only recently become affordable in schools and are continuing to evolve rapidly, the conditions under which they may be best used in classrooms are not yet well defined. However, there are several promising directions that may guide future research in this area. The design process involved in desktop manufacturing affords an opportunity for connections among multiple representations. The virtual design on the computer screen and the corresponding physical object that is produced are two representations of the same underlying construct. Negotiating these representations offers connections to mathematics taught in schools such as ratios, proportion, and scaling. Computer-assisted design programs developed as learning tools can capture information about student design choices and underlying thought processes. Construction of physical prototypes through desktop manufacturing involves extensive involvement of motor skills that may have linkages with student achievement. Digital objects and designs developed at one school can be disseminated via the Internet and reproduced at other sites, allowing designs to be shared and adapted for specific educational goals.

Keywords

Children’s engineering Digital fabrication Desktop manufacturing STEM 

References

  1. Ainsworth, S. (2006). DeFT: A conceptual framework for considering learning with multiple representations. Learning and Instruction, 16(3), 183–198.CrossRefGoogle Scholar
  2. Ainsworth, S., & Loizou, A. (2003). The effects of self-explaining when learning with text or diagrams. Cognitive Science, 27, 669–681.CrossRefGoogle Scholar
  3. Atman, C., Chimka, J. R., Bursic, K. M., & Nachtmann, H. N. (1999). A comparison of freshman and senior engineering design processes. Design Studies, 20(2), 131–152.CrossRefGoogle Scholar
  4. 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.CrossRefGoogle Scholar
  5. Bailey, R., & Szabo, Z. (2006). Assessing engineering design process knowledge. International Journal of Engineering Education, 22(3), 508–518.Google Scholar
  6. Barsalou, L. W. (2010). Grounded cognition: Past, present, and future. Topics in Cognitive Science, 2, 716–724.CrossRefGoogle Scholar
  7. Berry, R. Q., III, Bull, G., Browning, C., Thomas, C. D., Starkweather, K., & Aylor, J. H. (2010). Preliminary considerations regarding use of digital fabrication to incorporate engineering design principles in elementary mathematics education. Contemporary Issues in Technology and Teacher Education, 10(2), 167–172.Google Scholar
  8. Blikstein, P., & Wilensky, U. (2007). Bifocal modeling: a framework for combining computer modeling, robotics and real-world sensing. Paper presented at the annual meeting of the American Educational Research Association (AERA 2007), Chicago, USA.Google Scholar
  9. Blikstein, P., & Wilensky, U. (2010). Materialsim: a constructionist agent-based modeling approach to engineering education. In M.J. Jacobson and P. Reimann (Eds.), Designs for learning environments of the future: 17 International perspectives from the learning sciences (pp. 17–60). New York: Springer-Verlag.Google Scholar
  10. Bradsher, K. (2010). China drawing high-tech researcher from U.S. The New York Times. Retrieved from http://www.nytimes.com/2010/03/18/business/global/18research.html?ref=keithbradsher&_r=0.Google Scholar
  11. Bruner, J. S. (1966). Toward a theory of instruction. Cambridge, MA: Belkapp.Google Scholar
  12. Bull, G., & Groves, J. (2009). The democratization of production. Learning and Leading with Technology, 37(3), 36–37.Google Scholar
  13. *Bull, G., Knezek, G., & Gibson, D. (2009). A rationale for incorporating engineering education into the teacher education curriculum. Contemporary Issues in Technology and Teacher Education, 9(3), 222–225.Google Scholar
  14. Bull, G., Smith, S & Stearns, P. (2011, March). Fab@School: children’s engineering in the elementary classroom. Paper presented at the national conference of the Society for Information Technology and Teacher Education. Nashville, TN.Google Scholar
  15. Burghardt, M. D. (2000). Developing the field of children’s engineering, Paper presented at the ERM Division, ASEE 2000 Annual Conference, St. Louis.Google Scholar
  16. Burghardt, M. D., Hecht, D., Russo, M., Lauckhardt, J., & Hacker, M. (2010). A study of mathematics infusion in middle school technology education classes. Journal of Technology Education, 22(1), 58–74.Google Scholar
  17. Cantrell, P., Pekcan, G., Itani, A., & Velasquez-Bryant, N. (2006). The effects of engineering modules on student learning in middle school science classrooms. Journal of Engineering Education, 95(4), 301–309.CrossRefGoogle Scholar
  18. *Chiu, J. L., Bull, G., Berry, R. Q., & Kjellstrom, W. R. (2012). Teaching Engineering Design with Digital Fabrication: Imagining, Creating, and Refining Ideas. In N. Levine & C. Mouza (Eds.), Emerging Technologies for the Classroom: A Learning Sciences Perspective. New York, NY: Springer.Google Scholar
  19. Council, N. R. (2011). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. Washington, DC: The National Academies.Google Scholar
  20. Cunningham, C. M. (2009). Engineering is elementary. The Bridge, 30(3), 11–17.Google Scholar
  21. Davis, E. E., Pitchford, N. J., & Limback, E. (2011). The interrelation between cognitive and motor development in typically developing children aged 4–11 years is underpinned by visual processing and fine manual control. British Journal of Psychology, 102(3), 569–584.CrossRefGoogle Scholar
  22. de Koning, B. B., & Tabbers, H. K. (2011). Facilitating understanding of movements in dynamic visualizations: An embodied perspective. Educational Psychology Review, 23, 501–521.CrossRefGoogle Scholar
  23. Diamond, A. (2000). Close interrelation of motor development and cognitive development and of the cerebellum and prefrontal cortex. Child Development, 71, 44–56.CrossRefGoogle Scholar
  24. Diamond, A., & Lee, K. (2011). Interventions shown to aid executive function development in children 4 to 12 years old. Science, 333, 959–964.CrossRefGoogle Scholar
  25. Duncan, R. G., & Reiser, B. J. (2007). Reasoning across ontologically distinct levels: Students’ understandings of molecular genetics. Journal of Research in Science Teaching, 44(7), 938–959.CrossRefGoogle Scholar
  26. *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, 94(1), 103–120.Google Scholar
  27. Fortus, D., Dershimer, R. C., Krajcik, J., Marx, R. W., Mamlok, & Naaman, R. (2004). Design-based science and student learning. Journal of Research in Science Teaching, 41(10), 1081–1110.CrossRefGoogle Scholar
  28. Funk, S. G., Sturner, R. A., & Green, J. A. (1986). Preschool prediction of early school performance: Relationship of McCarthy scales of Children’s abilities prior to school entry to achievement in kindergarten, first and second grades. Journal of School Psychology, 24, 181–194.CrossRefGoogle Scholar
  29. *Gershenfeld, N. A. (2005). Fab: the coming revolution on your desktop—from personal computers to personal fabrication. New York, NY: Basic Books.Google Scholar
  30. Gibson, J. J. (1979). The ecological approach to visual perception. Boston, MA: Houghton Mifflin.Google Scholar
  31. Ginsburg, H. P., Klein, A., & Starkey, P. (1998). The development of children’s mathematical knowledge: Connecting research with ­practice. In I. E. Sigel & K. A. Renninger (Eds.), Handbook of child psychology (Child psychology in practice (5th Ed.), Vol. 4, pp. 401–476). New York, NY: Wiley & Sons.Google Scholar
  32. Glenberg, A. M. (1997). What memory is for. The Behavioral and Brain Sciences, 2, 1–55.Google Scholar
  33. Goldberg, A., Russell, M., & Cook, A. (2003). The effect of computers on student writing: a metaanalysis of studies from 1992 to 2002. Journal of Technology, Learning, and Assessment, 2(1), 1–47.Google Scholar
  34. Goldman, S. R. (2003). Learning in complex domains: When and why do multiple representations help? Learning and Instruction, 13(2), 239–244.CrossRefGoogle Scholar
  35. Grissmer, D., Grimm, K. J., Aiyer, S. M., Murrah, W. M., & Steele, J. S. (2010). Fine motor skills and early comprehension of the world: Two new school readiness indicators. Developmental Psychology, 46(5), 1008–1017.CrossRefGoogle Scholar
  36. Hayes, C. C., Goel, A. K., Tumer, I. Y., Agogino, A. M., & Regli, W. C. (2011). Intelligent support for product design: Looking backward, looking forward. Journal of Computing and Information Science in Engineering, 11(2), 021007.CrossRefGoogle Scholar
  37. Hickey, D. T., Kindfield, A. C. H., Horwitz, P., & Christie, M. A. T. (2003). Integrating curriculum, instruction, assessment, and evaluation in a technology-supported genetics learning environment. American Educational Research Journal, 40(2), 495.CrossRefGoogle Scholar
  38. Hmelo, C. E., Holton, D. L., & Kolodner, J. L. (2000). Designing to learn about complex systems. The Journal of the Learning Sciences, 9(3), 247–298.CrossRefGoogle Scholar
  39. Horwitz, P. (1995). Linking models to data: Hypermodels for science education. The High School Journal, 79(2), 148–156.Google Scholar
  40. Horwitz, P., & Christie, M. (1999). Hypermodels: Embedding curriculum and assessment in computer-based manipulatives. Journal of Education, 181, 1–24.Google Scholar
  41. Hsu, M., Cardella, M., & Purzer, S. (2010). Elementary students’ learning progressions and prior knowledge on engineering design process. Paper presented at the Annual Meeting of the National Association for Science Teaching.Google Scholar
  42. Hsu, M. C., Purzer, S., & Cardella, M. E. (2011). Elementary teachers’ views about teaching design, engineering, and technology. Journal of Pre-College Engineering Education Research (J-PEER), 1(2), 5.Google Scholar
  43. Jin, Y., & Chusilp, P. (2006). Study of mental iteration in different design situations. Design Studies, 27(1), 25–55.CrossRefGoogle Scholar
  44. Jin, Y., & Li, W. (2007). Design concept generation: A hierarchical coevolutionary approach. Journal of Mechanical Design, Transactions of the ASME, 129(10), 1012–1022.CrossRefGoogle Scholar
  45. Johnstone, A. H. (1991). Why is science difficult to learn? Things are seldom what they seem. Journal of Computer Assisted Learning, 7, 75–83.CrossRefGoogle Scholar
  46. Jonson, B. (2005). Design ideation: The conceptual sketch in the digital age. Design Studies, 26(6), 613–624.CrossRefGoogle Scholar
  47. Kaput, J., & Schorr, R. (2008). The case of SimCalc, algebra, and calculus. Research on Technology and the Teaching and Learning of Mathematics: Cases and Perspectives, 2, 211.Google Scholar
  48. *Katehi, L., Pearson, G., & Feder, M. (2009). Engineering in K-12 Education. Washington, DC: The National Academies Press.Google Scholar
  49. Kelley, T. R. (2008). Cognitive processes of students participating in engineering-focused design instruction. Journal of Technology Education, 19(2).Google Scholar
  50. Kolodner, J. L., Camp, P., Crismond, D., Fasse, B., Gray, J., Holbrook, J., et al. (2003). Problem-based learning meets case-based reasoning in the middle-school classroom: Putting learning by design into practice. The Journal of the Learning Sciences, 12, 495–547.CrossRefGoogle Scholar
  51. Kozma, R. (2000). The use of multiple representations and the social construction of understanding in chemistry. In M. Jacobson & R. Kozma (Eds.), Innovations in science and mathematics education: Advanced designs for technologies of learning (pp. 314–322). Mahwah, NJ: Erlbaum.Google Scholar
  52. Kozma, R. (2003). The material features of multiple representations and their cognitive and social affordances for science understanding. Learning and Instruction, 13(2), 205–226.CrossRefGoogle Scholar
  53. Lachapelle, C. P., & Cunningham, C. M. (2007). Engineering is elementary: Children’s changing understandings of science and engineering. American Society for Engineering Education Annual. Honolulu, HI: Conference & Exposition.Google Scholar
  54. Lakoff, G., & Johnson, M. (1980). Metaphors we live by. Chicago: The University of Chicago Press.Google Scholar
  55. Lakoff, G., & Núñez, R. E. (2000). Where mathematics comes from. New York, NY: Basic Books.Google Scholar
  56. Landy, J. M., & Burridge, K. (1999). Fundamental motor skills and movement activities for young children. New York, NY: Centre for Applied Research in Education.Google Scholar
  57. Leiner, H. C., Leiner, A. L., & Dow, R. S. (1993). The role of the cerebellum in the human brain. Trends in Neurosciences, 16(11), 453–454.CrossRefGoogle Scholar
  58. Lewis, J., & Wood-Robinson, C. (2000). Genes, chromosomes, cell division and inheritance-do students see any relationship? International Journal of Science Education, 22(2), 177–195.CrossRefGoogle Scholar
  59. *Lipson H., & Kurman, M., (2010). Factory@Home: The emerging economy of personal fabrication. Report commissioned by the White House Office of Science & Technology Policy.Google Scholar
  60. Lubinski, D. (2010). Spatial ability and STEM: a sleeping giant for talent identification and development. Personality and Individual Differences, 49, 344–351.CrossRefGoogle Scholar
  61. Luo, Z., Jose, P. E., Huntsinger, C. S., & Pigott, T. D. (2007). Fine motor skills and mathematics achievement in East Asian American and European American kindergartners and first graders. British Journal of Developmental Psychology, 25(4), 595–614.CrossRefGoogle Scholar
  62. Magill, F. N. (Ed.). (1996). International encyclopedia of psychology. London: Fitzroy Dearborn.Google Scholar
  63. Malone E., Lipson H., (2006) Fab@Home: the personal desktop fabricator kit, Proceedings of the 17th Solid Freeform Fabrication Symposium, Austin TX.Google Scholar
  64. Marbach-Ad, G., & Stavy, R. (2000). Students’ cellular and molecular explanations of genetic phenomena. Journal of Biological Education, 34(4), 200–205.CrossRefGoogle Scholar
  65. Mentzer, N., & Park, K. (2011). High school students as novice designers. Paper presented at the Annual Conference of the American Society for Engineering Education, Vancouver, BC.Google Scholar
  66. Moskal, B. M., Skokan, C., Kosbar, L., Dean, A., Westland, C., Barker, H., et al. (2007). K-12 outreach: Identifying the broader impacts of four outreach projects. Journal of Engineering Education, 96(3), 173–189.CrossRefGoogle Scholar
  67. National Academy of Engineering, 2012 Bernard M. Gordon Prize. Retrieved January 5, 2012, from http://www.nae.edu/Activities/Projects/Awards/GordonPrize
  68. National Science Board. (2007). National action plan for addressing the critical needs of the U.S. science, technology, engineering, and mathematics education system. Washington, DC: National Science Board.Google Scholar
  69. Park, G., Lubinski, D., & Benbow C. P. (2010). Recognizing spatial intelligence: our schools, and our society, must do more to ­recognize spatial reasoning, a key kind of intelligence. Scientific American, Retrieved from http://www.scientificamerican.com/article.cfm?id=recognizing-spatial-intel
  70. Rivoli, G. J., & Ralston, P. A. S. (2009). Elementary and middle school engineering outreach: Building a STEM pipeline. In B. Bernal, (Ed.), Proceedings of the 2009 ASEE Southeastern Section Conference. Retrieved from http://icee.usm.edu/ICEE/conferences/ASEE-SE-2010/Conference%20Files/ASEE2009/ASEE2009SE%20frame.htm.
  71. Robertson, B. F., & Radcliffe, D. F. (2009). Impact of CAD tools on creative problem solving in engineering design. Computer-Aided Design, 41(3), 136–146.CrossRefGoogle Scholar
  72. Rogers, C., & Portsmore, M. (2004). Bringing engineering to elementary school. Journal of STEM Education, 5(3–4), 17–28.Google Scholar
  73. Seitz, J. A. (2000). The bodily basis of thought. New Ideas in Psychology, 18(1), 23–40.CrossRefGoogle Scholar
  74. Silk, E. M., Schunn, C. D., & Strand-Cary, M. (2009). The impact of an engineering design curriculum on science reasoning in an urban setting. Journal of Science Education and Technology, 18(3), 209–223.CrossRefGoogle Scholar
  75. Smith, J. P., diSessa, A., & Roschelle, J. (1994). Misconceptions reconceived: A constructivist analysis of knowledge in transition. The Journal of the Learning Sciences, 3(2), 115–163.CrossRefGoogle Scholar
  76. Society of Manufacturing Engineering. (2009). SME unveils annual Innovations that could change the way you manufacture. Retrieved from http://www.sme.org/cgi-bin/get-press.pl?%26%2620090016%26PR%26%26SME%26
  77. Svihla, V., & Petrosino, A. J. (2008). Improving our understanding of K-12 engineering education. Paper presented at the International Conference on Engineering Education. Heraklion, Greece.Google Scholar
  78. Tseng, T., Bryant, C., & Blikstein, P. (2011) Mechanix: an interactive display for exploring engineering design through a tangible interface. Proceedings of Tangible and Embedded Interaction (IDC 2011), Madeira, Portugal.Google Scholar
  79. U.S. Department of Education. (2010). National Educational Technology Plan 2010: Transforming American Education: Learning Powered by Technology. Washington DC: Office of Educational Technology, U.S. Department of Education.Google Scholar
  80. Voelcker-Rehage, C. (2005). Der Zusammenhang zwischen motorischer und kognitiver Entwicklung im frühen Kindesalter – Ein Teilergebnis der MODALIS-Studie, [The relationship between motoric and cognitive development in early childhood - A partial result from the MODALIS Study]. Deutsche Zeitschrift für Sportmedizin, 56, 358–359.Google Scholar
  81. Woodbury, R., & Burrow, A. (2006). Whither design space. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 20(1), 63–82.Google Scholar
  82. Zucker, A. (2008). Transforming schools with technology: How smart use of digital tools helps achieve six key education goals. Cambridge, MA: Harvard Education Publishing Group.Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Glen Bull
    • 1
  • Jennifer Chiu
    • 1
  • Robert Berry
    • 1
  • Hod Lipson
    • 2
  • Charles Xie
    • 3
  1. 1.University of Virginia, Curry School of EducationCharlottesvilleUSA
  2. 2.Cornell UniversityIthacaUSA
  3. 3.The Concord ConsortiumConcordUSA

Personalised recommendations