Data-driven design education: big data analytics for architectural skills training

Machine learning, parametric design, and big data analytics are transforming architectural education by enabling performance prediction, iterative workflows, and large-scale data visualization. This study presents a data-driven framework that integrates these computational technologies to enhance architectural skills training. Machine learning models predict student performance and generate personalized learning pathways, while parametric design tools facilitate structural optimization and real-time design iteration. Big data analytics processes complex datasets, providing actionable insights through advanced visualization techniques. A case study involving 30 undergraduate architecture students demonstrates the framework's effectiveness, utilizing Python for data analysis, Grasshopper for parametric modeling, and Tableau for feedback visualization. Results show a 35% reduction in iteration time and a 28% improvement in design precision compared to traditional methods. The findings illustrate the framework's ability to bridge the gap between traditional education and computational demands, fostering creativity, improving spatial reasoning, and achieving precise, efficient design outcomes.