Effect of Computer Learning on Performance in Early Architecture Education

Authors

  • Vriddhi . School of Architecture, Christ University, Bangalore
  • Joy Sen Department of Architecture & Regional Planning, Indian Institute of Technology Kharagpur, West Bengal

DOI:

https://doi.org/10.16920/jeet/2022/v35i4/22103

Keywords:

Architecture Education, Sketching, Digital Tools, Digital Performance, Intuitive Interface, Mental Imagery.

Abstract

A mixed cohort of students with different experience backgrounds join the architecture degree. While some are well familiar with the user interface of computer and 3-D digital tools, others are not. The effect of such prior knowledge and their corresponding digital and analog performance in a designed experiment was evaluated with a sample of 38 first-year students. This was done to understand the performance effects of previous computer learning in students. Computer learning of the sample was studied in terms of years of computer exposure, the number of software known, and knowledge of 3D software or SketchUp. The results suggest that none of the factors contributed to the digital performance of students. This provided suggestions regarding the computer teaching emphasis which should be placed on students having less computer learning.

Downloads

Download data is not yet available.

Downloads

Published

2022-04-01

How to Cite

., V., & Sen, J. (2022). Effect of Computer Learning on Performance in Early Architecture Education. Journal of Engineering Education Transformations, 42–49. https://doi.org/10.16920/jeet/2022/v35i4/22103

Issue

Volume 35, Issue 4, April 2022

Section

Original Article
Crossref
Scopus
Google Scholar
Europe PMC

References

Baghaei Daemei, A., & Safari, H. (2018). Factors affecting creativity in the architectural education process based on computer-aided design. Frontiers of Architectural Research, 7(1), 100–106. https://doi.org/10.1016/j.foar.2017.09.001

Eastman, C., & Computing, D. (2001). New Directions in Design Cognition. Design Knowing and Learning: Cognition in Design Education, 147–198. doi:10.1016/b978-008043868-9/50008-5

Goldschmidt, G. (1994). On visual design thinking: The vis kids of architecture. Design Studies, 15(2), 158–174. https://doi.org/10.1016/0142-694X(94)90022-1

Goldschmidt, G. (1995) Visual displays for design: Imagery, analogy and databases of visual images, Ch-4, Visual databases in architecture: Recent advances in design and decision-making, Avebury. 53-74

Kavakli, M., Suwa, M., Gero, J., & Purcell, T. (1999). Sketching interpretation in novice and expert designers. (eds), Visual and Spatial Reasoning in Design, Key Centre of Design Computing and Cognition, University of Sydney, Sydney, Australia, pp. 209-220.

Kosslyn, S. M. (2005). Mental images and the brain. Cognitive Neuropsychology, 22(3/4), 333–347.

Lee, J., Ahn, J., Kim, J., Kho, J. M., & Paik, H. Y. (2018). Cognitive evaluation for conceptual design: Cognitive role of a 3D sculpture tool in the design thinking process. Digital Creativity, 29(4), 299–314. https://doi.org/10.1080/14626268.2018.1528988

Norman, D. A. (2013). The design of everyday things. MIT Press.

Pearson, J., Clifford, C. W. G., & Tong, F. (2008). The Functional Impact of Mental Imagery on Conscious Perception. Current Biology, 18(13), 982–986. https://doi.org/10.1016/j.cub.2008.05.048

Schön, D. A., & Wiggins, G. (1992). Kinds of Seeing in Designing. Creativity and Innovation Management, 1(2), 68–74. https://doi.org/10.1111/j.1467-8691.1992.tb00031.x

Zhang J. Patel V. L. (2006) Distributed cognition, representation, and affordance. Pragmatics & Cognition, 14(2), 333–341. https://doi.org/10.1075/pc.14.2.12zha