Abstraction–Reconstruction as a Lens for Computational Design Thinking: A Comparative Study of the Parametric Design Environment and Building Information Modeling-Based Modeling

The rapid adoption of building information modeling (BIM) in architecture and engineering necessitates pedagogical frameworks that integrate computational design thinking into BIM education. However, this significant paradigm shift which focuses on psychomotor enhancement is often regarded as incremental. This paper introduces Abstraction-Reconstruction, a novel framework grounded in Piaget’s formal operational stage theory and dual-process models of design and computational thinking, emphasizing iterative cycles of observation, abstraction, and algorithmic thinking. An empirical study was conducted involving 84 undergraduate students across multiple disciplines, of whom 50 were from Architecture, as part of a mini capstone project. The study compared parametric skill development across two environments, a Parametric Design Environment (PDE)/Visual Algorithm Editor (VAE) and a BIM system, using six performance indicators spanning Design Skills (DS) and Parametric Skills (PS), assessed with substantial inter-rater reliability (Fleiss’s K = 0.72). The BIM system outperformed PDE/VAE in parametric skills, yielding more consistent design skill scores centered around 8.0, attributed to its object-oriented structure that scaffolds abstraction through hierarchical data relationships. In PDE/VAE, Explicit Reasoning was the strongest predictor of parametric performance ( = 0.67, p < 0.001; R2 = 0.48–0.62), while Problem Definition was the dominant predictor in the BIM environment. Problem Abstraction and Parametric Generation demonstrated a near-perfect correlation (r = 0.96) in PDE/VAE, underscoring abstraction as the critical bridge between design and computational thinking. A post-course evaluation confirmed persistent challenges in generalizing abstraction while affirming that BIM fosters student confidence through structured workflows. This study validates Abstraction-Reconstruction as an effective pedagogical strategy, emphasizing the need to teach it as a dual cognitive process that integrates design intuition with algorithmic thinking to prepare students for evolving practices in the architecture, engineering, and construction industry.

Abstraction-Reconstruction; Building information modeling; Computational design thinking; Parametric modeling; Pedagogical framework

Aish, R., & Woodbury, R. (2005). Multi-level interaction in parametric design. Smart Graphics, 151–162. https://doi.org/10.1007/11536482_13

Ambrose, M. A. (2012). Agent provocateur - BIM in the academic design studio. International Journal of Architectural Computing, 10(1). https://doi.org/10.1260/1478-0771.10.1.53

Arisman, A., Widiastuti, I., Indraprastha, A., & Sudradjat, I. (2025). Creativity in generative design: The impact of architects’ control over geometric parameters in early-stage form-finding using genetic algorithms. International Journal of Architectural Computing. https://doi.org/10.1177/14780771251340203

Bhooshan, S. (2017). Parametric design thinking: A case-study of practice-embedded architectural research. Design Studies, 52, 115–143. https://doi.org/10.1016/j.destud.2017.05.003

Bilda, Z., & Demirkan, H. (2003). An insight on designers’ sketching activities in traditional versus digital media. Design Studies, 24. https://doi.org/10.1016/S0142-694X(02)00032-7

Bonani, A., Gennari, R., Melonio, A., & Rizvi, M. (2022). Design and computational thinking with IoTgo: What teachers think. Methodologies and Intelligent Systems for Technology Enhanced Learning, 12th International Conference, 165–174. https://doi.org/10.1007/978-3-031-20617-7_21

Burry, M. (2013). Scripting cultures: Architectural design and programming. Wiley.

Carpo, M. (2017). The second digital turn: Design beyond intelligence. MIT Press.

Chakraborty, P. (2024). Computer, computer science, and computational thinking: Relationship between the three concepts. Human Behavior and Emerging Technologies. https://doi.org/10.1155/2024/5044787

Cross, N. (2025). Designerly ways of knowing and thinking. Springer London. https://doi.org/10.1007/978-1-4471-7541-4

Csikszentmihalyi, M. (1996). Creativity: Flow and the psychology of discovery and invention. Harper Collins Publishers.

Davis, D. (2013). Modelled on software engineering: Flexible parametric models in the practice of architecture [Doctoral dissertation, RMIT University].

De Bono, E. (1993). Serious creativity: Using the power of lateral thinking to create new ideas. Harper Business.

Deleuze, G. (2017). Postscript on the societies of control. In Surveillance, crime and social control. Routledge. https://doi.org/10.4324/9781315242002-3

Deng, W., Guo, X., Cheng, W., & Zhang, W. (2022). Embodied design: A framework for teaching practices focused on the early development of computational thinking. Computer Applications in Engineering Education, 31(2), 365–375. https://doi.org/10.1002/cae.22588

Goldschmidt, G. (1991). The dialectics of sketching. Creativity Research Journal, 4(2), 123–143. https://doi.org/10.1080/10400419109534381

Haeusler, H., Hespanhol, L., & Hoggenmueller, M. (2018). Participationplus. Smart and Sustainable Built Environment, 7(1), 133–149. https://doi.org/10.1108/sasbe-10-2017-0049

Hales, D. (2025). The cacheian objectile: Design fictions of the furnishing of territories. Digital Creativity, 36(2), 153–165. https://doi.org/10.1080/14626268.2025.2494818

Hay, L., Duffy, A. H. B., Gilbert, S. J., Lyall, L. M., Campbell, G., Coyle, D., & Grealy, M. A. (2019). The neural correlates of ideation in product design engineering practitioners. Design Science, 5. https://doi.org/10.1017/dsj.2019.27

Hensel, M., Menges, A., & Weinstock, M. (2004). Emergence in architecture. Architectural Design, 74(3).

Holzer, D. (2015). BIM and parametric design in academia and practice: The changing context of knowledge acquisition and application in the digital age. International Journal of Architectural Computing, 13(1), 65–82. https://doi.org/10.1260/1478-0771.13.1.65

Jabi, B., W. and Johnson, & Woodbury, R. (2013). Parametric design for architecture. Laurence King Publishing.

Jacobus, F., Carpenter, A., Nunzio, A., & Badeschi, F. (2023). Architectonics and parametric thinking: Computational modeling for beginning design. Routledge. https://doi.org/10.4324/9781003252634

Kelly, N., & Gero, J. S. (2021). Design thinking and computational thinking: A dual process model for addressing design problems. Design Science, 7. https://doi.org/10.1017/dsj.2021.7

Klenner, N., Gemser, G., & Karpen, I. O. (2021). Entrepreneurial ways of designing and designerly ways of entrepreneuring: Exploring the relationship between design thinking and effectuation theory. Journal of Product Innovation Management, 39, 66–94. https://doi.org/10.1111/jpim.12587

Kolarevic, B. (2004). Architecture in the digital age: Design and manufacturing. Taylor & Francis. https://doi.org/10.4324/9780203634561

Kramer, J. (2007). Is abstraction the key to computing? Communications of the ACM, 50(4), 36–42. https://doi.org/10.1145/1232743.1232745

Lobach, K. (2021). Architectural object-states: The crisis of the architectural object. https://doi.org/10.32920/ryerson.14662707.v1

Meredith, M. (2008). From control to design: Parametric, algorithmic architecture. Verb.

Mueller, C. (2018). Handbook of design thinking [ResearchGate].

Ospina, J. M. G., & Sánchez, D. E. G. (2022). Design thinking traits and cognitive passive resistance: Mediating effect of linear thinking. Management Research Review. https://doi.org/10.1108/mrr-11-2021-0803

Oxman, R. (2006). Theory and design in the first digital age. Design Studies, 27(3), 229–265. https://doi.org/10.1016/j.destud.2005.11.002

Oxman, R. (2017). Thinking difference: Theories and models of parametric design thinking. Design Studies, 52, 4–39. https://doi.org/10.1016/j.destud.2017.06.001

Papert, S. (2020). Mindstorms: Children, computers, and powerful ideas. Basic Books.

Piaget, J. (2013). Child’s construction of quantities: Selected works Vol. 8. Routledge. https://doi.org/10.4324/9781315006253

Pospelov, K. N., Burlutskaya, Z. V., Gintciak, A. M., & Troshchenko, K. D. (2023). Multi-parametric optimization of complex system management scenarios based on simulation models. International Journal of Technology, 14(8), 1748–1758. https://doi.org/10.14716/ijtech.v14i8.6832

Pressman, A. (2019). Design thinking: A guide to creative problem solving for everyone. Routledge. https://doi.org/10.4324/9781315561936

Provost, L. P., & Sproul, R. M. (1996). Creativity and improvement: A vital link. Quality Progress, 101–107.

Rowe, P. G. (1987). Design thinking (1st ed.). MIT Press.

Rowe, P. G. (1991). Design thinking [New Edition]. MIT Press.

Schnabel, M. A., & Ham, J. J. (2012). Virtual design studio within a social network. Electronic Journal of Information Technology in Construction, 17, 397–415.

Schön, D. A. (1983). The reflective practitioner: How professionals think in action. Basic Books. https://doi.org/10.4324/9781315237473

Schön, D. (2017). The reflective practitioner: How professionals think in action. Routledge. https://doi.org/10.4324/9781315237473

Schumacher, P. (2009). Parametricism: A new global style for architecture and urban design. Architectural Design. https://doi.org/10.1002/ad.912

Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1016/0364-0213(88)90023-7

Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory. Springer.

Syafii, N. I. M., Ichinose, M., Kumakura, E., Jusuf, S., Hien, W., Chigusa, K., & Ashie, Y. (2021). Assessment of the water pond cooling effect on urban microclimate: A parametric study with numerical modeling. International Journal of Technology, 12(3), 461–471. https://doi.org/10.14716/ijtech.v12i3.4126

Tepehan, N. (2021). Designing objectiles as diagrams for movement. https://doi.org/10.7488/era/1555

Terzidis, K. (2006). Algorithmic architecture. Architectural Press. https://doi.org/10.4324/9780080461298

Villaverde, L., & Maneetham, D. (2024). Kinematic and parametric modeling of 6DOF industrial welding robot design and implementation. International Journal of Technology, 15(4), 1056–1070. https://doi.org/10.14716/ijtech.v15i4.6559

Wang, D., & Han, J. (2023). Exploring the impact of generative stimuli on the creativity of designers in combinational design. Proceedings of the Design Society. https://doi.org/10.1017/pds.2023.181

Wijaya, S., Utami, S., & Mangkuto, R. (2024). Multi-objective optimisation of skylight design parameters for a low-rise building in the tropics. International Journal of Technology, 15(4), 1012–1025. https://doi.org/10.14716/ijtech.v15i4.5484

Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49.

Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 366(1881). https://doi.org/10.1098/rsta.2008.0118

Woodbury, R. (2010). Elements of parametric design. Routledge.

Yu, R., Gu, N., & Ostwald, M. (2021). Computational design: Technology, cognition, and environments. CRC Press.

Zhang, J., Xie, H., & Li, H. (2017). Competency-based knowledge integration of BIM capstone in construction engineering and management education. International Journal of Engineering Education, 33(6), 2020–2032.