An exploration of the variables contributing to graphical education students’ CAD modelling capability

  • Thomas DelahuntyEmail author
  • Niall Seery
  • Rónán Dunbar
  • Michael Ryan


This paper reports on a study exploring the variables that contribute to upper second level students’ capability in a digital graphical modelling exercise in the field of technology education. The study evolves previous work in the area conducted in different contexts such as teacher education. Findings indicate deficiencies in second-level students’ digital modelling abilities and a significant relationship between students’ analytical, strategic and visuospatial abilities are presented. The paper discusses these findings as they relate to pedagogical reasoning processes and present the necessity to broaden the conception of graphical capability within digital CAD modelling contexts. Some key implications for technology education programmes and pedagogical approaches are discussed in conclusion.


Graphical education ICT CAD Pedagogy Capability 



  1. Ault, H. K. (2003). A comparison of solid modelling approaches. In American society for engineering education annual conference and exposition. Nashville.Google Scholar
  2. Baddeley, A. (2000). The episodic buffer: a new compnent of working memory? Trends in Cognitive Sciences, 4(11), 417–423.Google Scholar
  3. Baddeley, A., & Hitch, G. J. (1974). Working Memory. In G. H. Bower (Ed.), The psychology of learning and motivation. New York: Academic Press.Google Scholar
  4. Barr, R. E. (1999). Developing the EDG Curriculum for the 21st Century: a team effort. In ASEE annual conference and exposition. Charlotte.Google Scholar
  5. Bell, S. (2010). Project-based learning for the 21st century: Skills for the future. The Clearing House, 83(2), 39–43.Google Scholar
  6. Bhavnani, S., & John, B. (1997). From sufficient to efficient usage: an analysis of strategic knowledge. In Chi 97 conference proceedings (pp. 91–98). Atlanta: Georgia.Google Scholar
  7. Black, P., & Harrison, G. (1985). In place of confusion: Technology and science in the school curriculum. London: Nuffield-Chelsea Curriculum Trust and the National Centre for School Technology.Google Scholar
  8. Branoff, T. J., & Dobelis, M. (2014). Relationship between students’ spatial visualization ability and their ability to create 3D constraint-based models from various types of drawings. In 121st ASEE annual conference and exposition. Indianapolis.Google Scholar
  9. Cabeza, R., & Nyberg, L. (2000). Imaging cognition II: An empirical review of 275 PET and fMRI studies. Journal of Cognitive Neuroscience, 12(1), 1–47.Google Scholar
  10. Chester, I. (2007). Teaching for CAD expertise. International Journal of Technology and Design Education, 17(1), 23–35.Google Scholar
  11. Chester, I. (2008). 3D-CAD: Modern technology—Outdated pedagogy. Design and Technology Education: An International Journal, 12(1), 7–9.Google Scholar
  12. Cohen, J. W. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale: Lawrence Erlbaum Associates.Google Scholar
  13. Dakers, J. R. (2005). The hegemonic behaviorist cycle. International Journal of Technology and Design Education, 15(2), 111–126.Google Scholar
  14. D’Argembau, A., Ortoleva, C., Jumentier, S., & VanderLinden, M. (2010). Component processes underlying future thinking. Memory and Cognition, 38(6), 809–819.Google Scholar
  15. Delahunty, T., Seery, N., & Lynch, R. (2012). an evaluation of the assessment of graphical education at junior cycle in the Irish system. Design and Technology Education: An International Journal, 17(2), 9–20.Google Scholar
  16. Delahunty, T., Seery, N., & Lynch, R. (2015). Spatial skills and success in problem solving within engineering education. In 6th Research in Engineering Education Symposium DIT, July 13–15.Google Scholar
  17. Delahunty, T., Seery, N., & Lynch, R. (2018). Exploring the use of electroencephalography to gather objective evidence of cognitive processing during problem solving. Journal of Science Education and Technology, 27, 114–130.Google Scholar
  18. Dow, W. (2006). The need to change pedagogies in science and technology subjects: A European perspective. International Journal of Technology and Design Education, 16, 307–321.Google Scholar
  19. Edwards, A., Gilroy, P. & Hartley, D. (2002). Rethinking teacher education: An interdisciplinary analysis. London: Routledge Falmer.Google Scholar
  20. Ericsson, K. A., & Kintsch, W. (1995). Long-term working memory. Psychological Review, 102(2), 211.Google Scholar
  21. Ertmer, P. A., & Ottenbreit-Leftwich, A. T. (2010). Teacher technology change. Journal of Research on Technology in Education, 42(3), 255–284.Google Scholar
  22. Fang, Z. (1996). A review of research on teacher beliefs and practices. Educational Research, 38(1), 47–65.Google Scholar
  23. Field, D. A. (2004). Education and training for CAD in the auto industry. Computer-Aided Design, 36(14), 1431–1437.Google Scholar
  24. Fish, J., & Scrivener, S. (1990). Amplifying the mind’s eye: Sketching and visual cognition. Leonardo, 23(1), 117–126.Google Scholar
  25. Gagel, C. (2004). Technology profile: An assessment strategy for technological literacy. The Journal of Technology Studies, 30(4), 38–44.Google Scholar
  26. Garrison, R. D., & Akyol, Z. (2015). Toward the development of a metacognition construct for communities of inquiry. The Internet and Higher Education, 24, 66–71.Google Scholar
  27. Gibson, K. (2008). Technology and technological knowledge: A challenge for school curricula. Teachers and Teaching, 14(1), 3–15.Google Scholar
  28. Gimmestad, B. J. (1985). Using computer graphics for the development of spatial visualization. In American Society for Engineering education, p. 530.Google Scholar
  29. Goldschmidt, G. (2003). The backtalk of self-generated sketches. Design Issues, 19(1), 72–88.Google Scholar
  30. Guay, R. (1976). Purdue spatial vizualization test. Princeton: Educational testing service.Google Scholar
  31. Hassabis, D., & Maguire, E. A. (2007). Deconstructing episodic memory with construction. Trends in Cognitive Sciences, 11(7), 299–306.Google Scholar
  32. Hermans, R., Tondeur, J., vanBraak, J., & Valcke, M. (2008). The impact of primary school teachers’ educational beliefs on the classroom use of computers. Computers & Education, 51, 1499–1509.Google Scholar
  33. Johnson, M. D., & Diwakaran, R. P. (2011). An educational exercise examining the role of model attributes on the creation and alteration of CAD models. Computers & Education, 57, 1749–1761.Google Scholar
  34. Kimbell, R. (2004). Ideas and ideation. The Journal of Design and Technology Education, 9(3), 136–137.Google Scholar
  35. Levin, T., & Wadmany, R. (2005). Changes in educational beliefs and classroom practices of teachers and students in rich technology-based classrooms. Technology, Pedagogy and Education, 14(3), 281–308.Google Scholar
  36. Mcgarr, O. (2011). The elephant in the room: the influence of prevailing pedagogical practice on the integration of Design and Communication Graphics in the post-primary classroom. In E. Norman & N. Seery (Eds.), Graphicacy and Modelling. UK: Loughborough.Google Scholar
  37. McGarr, O., & Seery, N. (2011). Parametric pedagogy: Integrating parametric CAD in Irish post-primary schools. Design and Technology Education: An International Journal, 16(2), 57–66.Google Scholar
  38. NCCA (2007). Leaving Certificate Design and Communication Graphics Syllabus, Dublin.Google Scholar
  39. Norris, K., Sullivan, T., Poirot, J., & Soloway, E. (2003). No access, no use, no impact: snapshot surveys of educational technology in K-12. Journal of Research on Technology in Education, 36(1), 15–27.Google Scholar
  40. Orlando, J. (2009). Understanding changes in teachers’ ICT practices: A longitudinal perspective. Technology, Pedagogy and Education, 18(1), 33–44.Google Scholar
  41. Owen-Jackson, G. (2000). Design and technology in the school curriculum. In G. Owen-Jackson (Ed.), Learning to Teach Design and Technology in the secondary school (pp. 1–9). London: Routledge Falmer.Google Scholar
  42. Pintrich, P. R. (2002). The role of metacognitive knowledge in learning, teaching and assessing. Theory into Practice, 41(4), 219–225.Google Scholar
  43. Prawat, R. S. (1992). Teachers’ beliefs about teaching and learning: A constructivist perspective. American Journal of Education, 100(3), 354–395.Google Scholar
  44. Rodriguez, J., Ridge, J., Dickinson, A., & Whitwam, R. (1998). CAD training using interactive computer sessions. In American Society for Engineering Education annual conference and exposition conference proceedings.Google Scholar
  45. Rynne, A., Gaughran, W. F., & Seery, N. (2011). Defining the variables that contribute to developing 3D CAD modelling expertise. In E. Norman, & N. Seery (Eds.), Graphicacy and modelling (pp. 161–178). Loughborough.Google Scholar
  46. Schacter, D. L., & Addis, D. R. (2007). ‘The cognitive neuroscience of constructive memory: Remembering the past and imagining the future. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 362, 773–786.Google Scholar
  47. Seery, N., Lynch, R., & Dunbar, R. (2011). A review of the nature, provision and progression of graphical education in Ireland. In E. Norman, & N. Seery (Eds.), Graphicacy and modelling. Loughborough.Google Scholar
  48. Shulman, L. (1987). Knowledge and teaching: Foundations of the new reform. Harvard Educational Review, 57, 1–22.Google Scholar
  49. Sorby, S. (2000). Spatial abilities and their relationship to effective learning of 3-D modeling software. Engineering Design Graphics Journal, 64(3), 30–35.Google Scholar
  50. Sorby, S. A. (2007). Developing 3D spatial skills for engineering students. Australasian Journal of Engineering Education, 13(1), 1–11.Google Scholar
  51. Sorby, S. (2009). Educational research in developing 3-D spatial skills for engineering students. International Journal of Science Education, 31(3), 459–480.Google Scholar
  52. Stillings, N. A., Weisler, S. E., Chase, C. H., Feinstein, M. H., Garfield, J. L., & Rissland, E. L. (1995). Cognitive science: An introduction. London: MIT Press.Google Scholar
  53. Sweller, J., vanMerrienboer, J. J. G., & Paas, F. G. W. C. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10(3), 251–296.Google Scholar
  54. Todd, P. M., & Gigerenzer, G. (2000). Précis of simple heuristics that make us smart. Behavioral and Brain Sciences, 23, 727–780.Google Scholar
  55. Wai, J., Lubinski, D., & Benbow, C. P. (2009). Spatial ability for STEM domains: Aligning over 50 years of cumulative psychological knowledge solidifies its importance. Journal of Educational Psychology, 101(4), 817.Google Scholar
  56. Williams, P. J. (2009). Technological literacy: A multiliteracies approach for democracy. International Journal of Technology and Design Education, 19(3), 237–254.Google Scholar
  57. Williams, J., Iglesias, J., & Barak, M. (2008). Problem based learning: Application to technology education in three countries. International Journal of Technology and Design Education, 18, 319–335.Google Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.School of EducationUniversity College CorkCorkIreland
  2. 2.Athlone Institute of TechnologyAthloneIreland
  3. 3.Institute of Applied TechnologyAbu DhabiUnited Arab Emirates

Personalised recommendations