Implementing the 4C/ID Model for Parametric Design in Architectural Education

Öz

Parametric design (PD) education requires an algorithms-based logic that imposes considerably high cognitive load. The aim of this study, therefore, is to explore and determine the efficiency of the four-component instructional design model (4C/ID). The 4C/ID has a focus on authentic, whole-task learning, which was tested on a group of 14 senior architecture students on a 15-week undergraduate course on PD within mixed-design research. There were pre- and post-assessments of computational thinking (CT), CT self-efficacy (CTSE), and design thinking (DT), along with analyses of final projects and qualitative feedback. The quantitative data showed that there were significant gains for algorithmic thinking and problem-solving skills after taking the course. There were significant gains for CTSE factors such as abstraction and reasoning. On correlation analysis, there was a strong relationship between course performance and these technical skills, although there were no significant correlations with DT scores, which were found not to have changed much. This implies that though the 4C/ID model facilitated the transition of students from passive understanding to confident performance of procedures, achieving competencies in higher order design skills took longer. Qualitative feedback confirmed the effectiveness of the 4C/ID model but also highlighted two issues: visual complexity in the interface and data complexity, which could hinder students who are not sufficiently prepared. Therefore, effective PD education needs careful task design and support for logical reasoning to balance technical requirements with creative objectives.

Anahtar Kelimeler

• Parametric Design Education
• 4C/ID Model
• Computational Thinking
• Algorithmic Reasoning
• Instructional Design

Etik Beyan

The ethical approval for this research was granted by the Yildiz Technical University Social and Human Sciences Research Ethics Committee (Meeting Date: 03.05.2025, Decision No: 2025.05).

Mimarlık Eğitiminde Parametrik Tasarım için 4B/ÖT Modelinin Uygulanması

Öz

Parametrik tasarım (PT) eğitimi, oldukça yüksek bilişsel yük getiren algoritma tabanlı bir mantık gerektirir. Bu nedenle bu çalışmanın amacı, dört bileşenli öğretim tasarım modelinin (4B/ÖT) verimliliğini araştırmak ve belirlemektir. 4B/ÖT; otantik, bütünsel görev öğrenimine odaklanmaktadır ve karma tasarım araştırması kapsamında 15 haftalık bir PT lisans dersinde 14 son sınıf mimarlık öğrencisi üzerinde test edilmiştir. Bilgi işlemsel düşünme (BİD), bilgi işlemsel düşünme öz-yeterliği (BİDÖ) ve tasarım odaklı düşünme (TOD) ön ve son değerlendirmeleri ile, final projeleri ve nitel geri bildirim analizleri yapılmıştır. Nicel veriler, kursun ardından algoritmik düşünme ve problem çözme becerilerinde önemli kazanımlar olduğunu göstermiştir. Soyutlama ve akıl yürütme gibi BİDÖ faktörlerinde önemli kazanımlar olmuştur. Korelasyon analizinde, öğrenme performansı ile bu teknik beceriler arasında güçlü bir ilişki olduğu görülmüştür; ancak TOD puanları ile önemli bir korelasyon bulunmamıştır ve bu puanların fazla değişmediği tespit edilmiştir. Bu; 4B/ÖT modelinin, öğrencilerin pasif anlayıştan prosedürlerin kendinden emin bir şekilde uygulanmasına geçişini kolaylaştırmasına rağmen üst düzey tasarım becerilerinde yetkinlik kazanmanın daha uzun sürdüğünü ima etmektedir. Niteliksel geri bildirimler, 4B/ÖT modelinin etkinliğini doğrulamakta, ancak aynı zamanda iki sorunu da vurgulamaktadır: arayüzdeki görsel karmaşıklık ve veri karmaşıklığı. Bu da yeterince hazırlıklı olmayan öğrencileri engelleyebilir. Bu nedenle etkili PT eğitimi, teknik gereksinimleri yaratıcı hedeflerle dengelemek için dikkatli görev tasarımı ve mantıksal akıl yürütme desteği gerektirir.

Anahtar Kelimeler

• Parametrik Tasarım Eğitimi
• 4B/ÖT Modeli
• Bilgi İşlemsel Düşünme
• Algoritmik Akıl Yürütme
• Öğretim Tasarımı

Etik Beyan

Bu araştırmanın etik onayı Yıldız Teknik Üniversitesi Sosyal ve Beşeri Bilimler Araştırma Etik Kurulu tarafından verilmiştir (Toplantı Tarihi: 03.05.2025, Karar No: 2025.05).

Kaynakça

1. Abdelmohsen, S., & Massoud, P. (2021). A material-based computation framework for parametric design education. Open House International, 46(3), 459–475. https://doi.org/10.1108/OHI-02-2021-0043
2. Alalouch, C. (2018). A pedagogical approach to integrate parametric thinking in early design studios. Archnet-IJAR: International Journal of Architectural Research, 12(2), 162–181. https://doi.org/10.26687/archnet-ijar.v12i2.1584
3. Amabile, T. M. (1982). Social psychology of creativity: A consensual assessment technique. Journal of Personality and Social Psychology, 43(5), 997. https://doi.org/10.1037/0022-3514.43.5.997
4. Brenner, W., Uebernickel, F., & Abrell, T. (2016). Design thinking as mindset, process, and toolbox: Experiences from research and teaching at the University of St. Gallen. In W. Brenner & F. Uebernickel (Eds.), Design thinking for innovation: Research and practice (pp. 3–21). Springer. https://doi.org/10.1007/978-3-319-26100-3
5. Buitrago Flórez, F., Casallas, R., Hernández, M., Reyes, A., Restrepo, S., & Danies, G. (2017). Changing a generation’s way of thinking: Teaching computational thinking through programming. Review of Educational Research, 87(4), 834–860. https://doi.org/10.3102/0034654317710096
6. Çam, E., & Özen, Y. A. (2021). Mimarlık temel eğitiminde kavramsal tasarım sürecinde algoritmik düşünme sisteminin etkisini incelemeye yönelik bir çalışma. In A. Çağlar (Ed.), Mimarlık Alanında Farklı Yaklaşımlar (pp. 59–74). Nobel Bilimsel Eserler.
7. Cho, J. Y., & Suh, J. (2019). Understanding spatial ability in interior design education: 2D-to-3D visualization proficiency as a predictor of design performance. Journal of Interior Design, 44(3), 141–159. https://doi.org/10.1111/joid.12143
8. Delahunty, T., Seery, N., Dunbar, R., & Ryan, M. (2020). An exploration of the variables contributing to graphical education students’ CAD modelling capability. International Journal of Technology and Design Education, 30(2), 389–411. https://doi.org/10.1007/s10798-019-09503-x

9. Frerejean, J., van Merriënboer, J. J. G., Kirschner, P. A., Roex, A., Aertgeerts, B., & Marcellis, M. (2019). Designing instruction for complex learning: 4C/ID in higher education. European Journal of Education, 54(4), 513–524. https://doi.org/doi.org/10.1111/ejed.12363
10. Harding, J. E., & Shepherd, P. (2017). Meta-parametric design. Design Studies, 52, 73–95. https://doi.org/10.1016/j.destud.2016.09.005
11. Henderson, P. B., Cortina, T. J., Hazzan, O., & Wing, J. M. (2007). Computational thinking. Proceedings of the 38th SIGCSE Technical Symposium on Computer Science Education, 195–196. https://doi.org/10.1145/1227310.1227378
12. Janesarvatan, F., & Van Rosmalen, P. (2023). Instructional design of virtual patients in dental education through a 4C/ID lens: a narrative review. Journal of Computers in Education, 11, 523–556. https://doi.org/10.1007/s40692-023-00268-w
13. Kavousi, S., Miller, P. A., & Alexander, P. A. (2020). Modeling metacognition in design thinking and design making. International Journal of Technology and Design Education, 30(4), 709–735. https://doi.org/10.1007/s10798-019-09521-9
14. Korkmaz, Ö., & Bai, X. (2019). Adapting computational thinking scale (CTS) for Chinese high school students and their thinking scale skills level. Participatory Educational Research, 6(1), 10–26. https://doi.org/10.17275/per.19.2.6.1
15. Kukul, V., & Karatas, S. (2019). Computational thinking self-efficacy scale: Development, validity and reliability. Informatics in Education, 18(1), 151–164. https://doi.org/10.15388/infedu.2019.07
16. Lee, J. H., Gu, N., Ostwald, M. J., & Jupp, J. (2013). Understanding cognitive activities in parametric design. In J. Zhang & C. Sun (Eds.), CCIS (Vol. 369, pp. 38–49). Springer. https://doi.org/10.1007/978-3-642-38974-0_4
17. Limbu, B. H., Jarodzka, H., Klemke, R., & Specht, M. (2018). Using sensors and augmented reality to train apprentices using recorded expert performance: A systematic literature review. Educational Research Review, 25, 1–22. https://doi.org/10.1016/j.edurev.2018.07.001
18. Li, Q., Liu, Z., Wang, P., Wang, J., & Luo, T. (2023). The influence of art programming courses on design thinking and computational thinking in college art and design students. Education and Information Technologies, 28(9), 10885–10902. https://doi.org/10.1007/s10639-023-11618-7
19. Ostrowska-Wawryniuk, K., Strzała, M., & Słyk, J. (2022). Form follows parameter: Algorithmic-thinking-oriented course for early-stage architectural education. Nexus Network Journal, 24(2), 503–522. https://doi.org/10.1007/s00004-022-00603-1
20. Oxman, R. (2008). Performance-based design: Current practices and research issues. International Journal of Architectural Computing, 6(1), 1–17. https://doi.org/10.1260/147807708784640090
21. Peteinarelis, A., & Yiannoudes, S. (2018). Parametric models and algorithmic thinking in architectural education. International Conference on Education and Research in Computer Aided Architectural Design in Europe, 401–410. https://doi.org/10.52842/conf.ecaade.2018.2.401
22. Razzouk, R., & Shute, V. (2012). What is design thinking and why is it important? Review of Educational Research, 82(3), 330–348. https://doi.org/10.3102/0034654312457429
23. Runco, M. A. (1988). Creativity research: Originality, utility, and integration. Creativity Research Journal, 1(1), 1–7. https://doi.org/10.1080/10400418809534283
24. Suh, J., & Cho, J. Y. (2020). Linking spatial ability, spatial strategies, and spatial creativity: A step to clarify the fuzzy relationship between spatial ability and creativity. Thinking Skills and Creativity, 35. https://doi.org/10.1016/j.tsc.2020.100628
25. Tan, F. (2024). Translation of form: A model for teaching design coding to undergraduate students. Nexus Network Journal, 26(4), 897–924. https://doi.org/10.1007/s00004-024-00778-9
26. Tsai, M.-J., & Wang, C.-Y. (2021). Assessing young students’ design thinking disposition and its relationship with computer programming self-efficacy. Journal of Educational Computing Research, 59(3), 410–428. https://doi.org/10.1177/0735633120967326
27. Uçar, U. E., Gökdemir, G., Güzelci, O. Z., & Garip, E. (2023). Integrating digital and manual modes of design. Periodica Polytechnica Architecture, 54(1), 37–49. https://doi.org/10.3311/ppar.21353
28. Vandewaetere, M., Manhaeve, D., Aertgeerts, B., Clarebout, G., Van Merriënboer, J. J. G., & Roex, A. (2015). 4C/ID in medical education: How to design an educational program based on whole-task learning: AMEE Guide No. 93. Medical Teacher, 37(1), 4–20. https://doi.org/10.3109/0142159X.2014.928407
29. van Merriënboer, J. J. G., & Kester, L. (2012). The four-component instructional design model: Multimedia principles in environments for complex learning. In The Cambridge Handbook of Multimedia Learning (pp. 71–94). Cambridge University Press. https://doi.org/10.1017/cbo9780511816819.006
30. van Merriënboer, J. J. G., & Kirschner, P. A. (2012). Ten steps to complex learning: A systematic approach to four-component instructional design. Routledge. https://doi.org/10.4324/9780203096864
31. Vazquez, E. (2024). Teaching parametric design: fostering algorithmic thinking through incomplete recipes. Open House International, 49(4), 736–751. https://doi.org/10.1108/OHI-06-2023-0135
32. Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35. https://doi.org/10.1145/1118178.1118215