| id |
ecaade2018_111 |
| authors |
Khean, Nariddh, Fabbri, Alessandra and Haeusler, M. Hank |
| year |
2018 |
| title |
Learning Machine Learning as an Architect, How to? - Presenting and evaluating a Grasshopper based platform to teach architecture students machine learning |
| doi |
https://doi.org/10.52842/conf.ecaade.2018.1.095
|
| source |
Kepczynska-Walczak, A, Bialkowski, S (eds.), Computing for a better tomorrow - Proceedings of the 36th eCAADe Conference - Volume 1, Lodz University of Technology, Lodz, Poland, 19-21 September 2018, pp. 95-102 |
| summary |
Machine learning algorithms have become widely embedded in many aspects of modern society. They have come to enhance systems, such as individualised marketing, social media services, and search engines. However, contrasting its growing ubiquity, the architectural industry has been comparatively resistant in its adoption; objectively one of the slowest industries to integrate with machine learning. Machine learning expertise can be separate from professionals in other fields; however, this separation can be a major hinderance in architecture, where interaction between the designer and the design facilitates the production of favourable outcomes. To bridge this knowledge gap, this research suggests that the solution lies with architectural education. Through the development of a novel educative framework, the research aims to teach architecture students how to implement machine learning. Exploration of student-centred pedagogical strategies was used to inform the conceptualisation of the educative module, which was subsequently implemented into an undergraduate computational design studio, and finally evaluated on its ability to effectively teach designers machine learning. The developed educative module represents a step towards greater technological adoption in the architecture industry. |
| keywords |
Artificial Intelligence; Machine Learning; Neural Networks; Student-Centred Learning; Educative Framework |
| series |
eCAADe |
| email |
|
| full text |
 file.pdf (248,091 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 07:52 |
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