Towards a Deep Learning Framework for the Architecture Studio: Empirical Validation of a Course-Level Project-Based Learning Model

Abstract

Architecture education is fundamentally centered on the design studio, a critical space for student learning and problem-solving. A conventional studio (referred to in this research as the non-PBL studio) follows a critiquing method where students work individually on semester-long projects, often ignoring stakeholder involvement and thus lacking in design innovation. In contrast, Problem and Project-Based Learning (PBL) frameworks, widely used in medical and engineering education, address these limitations. A Course Level Project-Based Learning (CLPBL) model is designed and implemented across two consecutive studio cycles at a private university in Bengaluru, India. Student performance from the two consecutive cycles of CLPBL vis-à-vis earlier cycles of the non-PBL method is analyzed and compared at the third year of an undergraduate architecture program. A mixed-methods approach is adopted to establish Deep Learning using two indicators. Empirical validation of students’ scores, done using the statistical tool R and analyzed for significance using an ANOVA, Student’s t-test, and Wilcoxon tests. Questionnaire surveys, used to elicit responses on Deep Learning and greater student satisfaction in CLPBL, are validated using the same statistical tools. The model identifies 5 indices of Deep Learning (DL) drawn from the implemented CLPBL, which align with PBL attributes for Engineering, but are tailored to Architecture Studio learning. In conclusion, the paper suggests a Deep Learning Studio framework for replication in architecture education, while remaining within the prescribed guidelines of the Council of Architecture and National Education Policy 2020, in the Indian context.