Large data for design research: An educational technology framework for studying design activity using a big data approach

Abstract

The complexity of design problems compels the collection of rich process data to understand designers. While some methods exist for capturing detailed process data (e.g., protocol studies), design research focused on design activities still faces challenges, including the scalability of these methods and technology transformations in industry that require new training. This work proposes the Large Data for Design Research (LaDDR) framework, which seeks to integrate big data properties into platforms dedicated to studying design practice and design learning to offer a new approach for capturing process data. This technological framework has three design principles for transforming design platforms: broad simulation scope, unobtrusive logging and support for creation and analysis actions. The case is made that LaDDR platforms will lead to three affordances for research and education: capturing design activities, context setting and operationalization, and research design scalability. Big data and design expertise are reviewed to show how this approach builds on past work. Next, the framework and affordances are presented. Three previously published studies are presented as cases to illustrate the ways in which a LaDDR platform’s affordances manifest. The discussion covers how LaDDR platforms can address the aforementioned challenges, including advancing human-technology collaboration and how this approach can be extended to other design platforms.