Teaching Data Science Students to Sketch Privacy Designs through Heuristics (Extended Technical Report)
Jinhe Wen, Yingxi Zhao, Wenqian Xu, Yaxing Yao, Haojian Jin
TL;DR
This study investigates how to teach data science students to communicate privacy designs via sketches. It introduces three simple heuristics—Device-Based Data Flow, Stakeholder Interactions with Data Flow, and Multi-Layered Representation—delivered at study start, and evaluates them against vocabulary-based sketching. Across two creation tasks and two interpretation tasks with 24 participants, the heuristic approach improves design coverage, reduces mental workload, and enhances readability and interpretability of sketches. The results advocate for a heuristic-focused, multi-layered sketching approach to improve privacy design communication in data science education and suggest avenues for broader adoption and future refinement.
Abstract
Recent studies reveal that experienced data practitioners often draw sketches to facilitate communication around privacy design concepts. However, there is limited understanding of how we can help novice students develop such communication skills. This paper studies methods for lowering novice data science students' barriers to creating high-quality privacy sketches. We first conducted a need-finding study (N=12) to identify barriers students face when sketching privacy designs. We then used a human-centered design approach to guide the method development, culminating in three simple, text-based heuristics. Our user studies with 24 data science students revealed that simply presenting three heuristics to the participants at the beginning of the study can enhance the coverage of privacy-related design decisions in sketches, reduce the mental effort required for creating sketches, and improve the readability of the final sketches.