Micro-entries: Encouraging Deeper Evaluation of Mental Models Over Time for Interactive Data Systems
This work addresses the problem of improving transparency and explainability in data systems for researchers and designers, though it is incremental as it builds on existing evaluation methods.
The paper tackles the challenge of evaluating users' evolving mental models in interactive data systems by reviewing existing measurement techniques and proposing guidelines for deeper, time-ordered evaluation, enabling researchers to track how user understanding develops through system interactions.
Many interactive data systems combine visual representations of data with embedded algorithmic support for automation and data exploration. To effectively support transparent and explainable data systems, it is important for researchers and designers to know how users understand the system. We discuss the evaluation of users' mental models of system logic. Mental models are challenging to capture and analyze. While common evaluation methods aim to approximate the user's final mental model after a period of system usage, user understanding continuously evolves as users interact with a system over time. In this paper, we review many common mental model measurement techniques, discuss tradeoffs, and recommend methods for deeper, more meaningful evaluation of mental models when using interactive data analysis and visualization systems. We present guidelines for evaluating mental models over time that reveal the evolution of specific model updates and how they may map to the particular use of interface features and data queries. By asking users to describe what they know and how they know it, researchers can collect structured, time-ordered insight into a user's conceptualization process while also helping guide users to their own discoveries.