A Data-Driven NLP Approach to Analyzing Framing and Reframing in Design Protocols

This study introduces a novel data-driven approach to quantitatively characterize and measure framing and reframing (F-RF) behaviors during design problem-solving. F-RF are cognitive processes which shape problem understanding and solution development in design. Quantitative measurement methods for F-RF remain largely unexplored. The proposed approach utilizes protocol analysis combined with Natural Language Processing (NLP) algorithms to track the occurrences and re-occurrences of design concepts expressed verbally while designing. Specifically, NLP algorithms are employed to identify F-RF, enabling the systematic tracking of F-RFs and their corresponding semantic values. By calculating the semantic value of concepts and frames, the approach enables determining how a concept and a frame differed from the previous occurrences. A case study of an architect and a student demonstrates this data-driven approach. The proposed methodology holds potential for the development of systems capable of providing real-time feedback to students and professional designers, supporting and enhancing their framing skills during the design process.