Panopticon: The Design and Evaluation of a Game that Teaches Data Science Students Designing Privacy
Authors: Yuhe Tian (University of California, San Diego), Shao-Yu Chu (University of California, San Diego), Yuxuan Liu (University of California, San Diego), Haojian Jin (University of California, San Diego)
Volume: 2025
Issue: 3
Pages: 398–414
DOI: https://doi.org/10.56553/popets-2025-0105
Abstract: In this paper, we describe the design and evaluation of Panopticon, an educational board game that helps data science students learn the skills of designing privacy-sensitive data practices with fun. Panopticon draws inspiration from the classic economics-themed game Monopoly, but re-imagines Monopoly’s financial system as a data economy and requires players to conduct privacy design related activities as they navigate the game board. We used two learning science principles, peer learning and formative feedback, to guide the game design. We evaluated the game through a user study with 36 players (i.e., 12 game sessions) and compared their learning outcomes to a control group (n=36) who learned privacy design through paper content. To measure the learning outcomes, we developed rubrics to quantitatively assess the quality of the privacy designs, covering the level of detail, the technical feasibility, and the empathy for stakeholders. Our results suggest that Panopticon increased the learning outcomes by 354%, with significant improvements in all three dimensions. Participants also reported it as an entertaining way to learn in the post-study interview.
Keywords: privacy education, educational game, data practice design
Copyright in PoPETs articles are held by their authors. This article is published under a Creative Commons Attribution 4.0 license.
