Due to the pandemic conditions, hands-on courses in architectural education were conducted remotely and their presentation had to be reconsidered. Hands-on courses, by their nature, support learning and learner-instructor interaction in the classroom environment. It was necessary to develop innovative solutions to ensure this interaction in virtual classrooms. This study discusses a method we experienced in the 2021-2022 spring semester of the "Principles of Digital Design and Fabrication in Architecture" course given at Gazi University. To combat potential interaction deficits, "problem-based learning (PBL)" and "learning by doing (LBD)" teaching methods were applied. While reflecting on foreseen problems in the curriculum on the students, we determined the distance education process causes different reflections on students in terms of digital modelling and fabrication techniques. All constraints and problem determinations obtained by the students were classified and a way to solve these problems developed with the LBD style. By the end of the course, the students, who were expected to design a small 3D object, first designed the mould then realised their fabrication of the object. In this process, while the foreseen problems and curriculum determined at the beginning overlapped, other problem determinations and their reflections formed an important base for the future curriculum.
Through the recent technological developments within the fourth industrial revolution, artificial intelligence (AI) studies have had a huge impact on various disciplines such as social sciences, information communication technologies (ICTs), architecture, engineering, and construction (AEC). Regarding decision-making and forecasting systems in particular, AI and machine learning (ML) technologies have provided an opportunity to improve the mutual relationships between machines and humans. When the connection between ML and architecture is considered, it is possible to claim that there is no parallel acceleration as in other disciplines. In this study, and considering the latest breakthroughs, we focus on revealing what ML and architecture have in common. Our focal point is to reveal common points by classifying and analyzing current literature through describing the potential of ML in architecture. Studies conducted using ML techniques and subsets of AI technologies were used in this paper, and the resulting data were interpreted using the bibliometric analysis method. In order to discuss the state-of-the-art research articles which have been published between 2014 and 2020, main subjects, subsets, and keywords were refined through the search engines. The statistical figures were demonstrated as huge datasets, and the results were clearly delineated through Sankey diagrams. Thanks to bibliometric analyses of the current literature of WOS (Web of Science), CUMINCAD (Cumulative Index about publications in Computer Aided Architectural Design supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD, and CAAD futures), predictable data have been presented allowing recommendations for possible future studies for researchers.
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