The rapid advancement of big data technology has positioned it as a pivotal force in the educational domain, particularly in the field of architectural education. Given the dual emphasis on practicality and theory within architectural education, there is a heightened demand for scientific and precise teaching decision-making. This study focuses on the development and application of a big data-based decision support system for architectural education informatics. Through comprehensive demand analysis, the study identifies the data support and personalized learning needs of educators and students in the teaching process. The system architecture encompasses data collection, preprocessing, analysis and mining, visualization, and decision support. Empirical results demonstrate that the system effectively enhances the decision-making process for educators, optimizes teaching strategies, and improves teaching quality and student learning outcomes. Moreover, the system’s visualization and personalized learning path recommendation functions provide students with more precise learning support, thereby fostering the development of personalized and precise education.
Hongfei Yu (Mon,) studied this question.