Model Literacy: An Extra Summative Evaluation Factor for Visual Analytics

2026-08-19

AI Summary

研究探讨了模型素养作为视觉分析性能的额外评价因素，通过控制实验发现模型-任务准确性和视觉分析任务准确性之间存在正相关关系。

Abstract

Understanding and enhancing visual analytics (VA) performance is important for maximizing their impact. Existing studies have successfully applied well-established summative evaluation methods from information visualization to the VA context, yet the recent emphasis on an extra data analysis/modeling stage in the VA pipeline poses an additional challenge. Inspired by the modern concept of visualization literacy, this paper examines model literacy, namely users' knowledge of the analysis model used in a VA technique, as an additional factor for VA performance. Results from a controlled study on the visual analysis of multidimensional data with two dimensionality-reduction models indicate a positive correlation between model-task accuracy and VA-task accuracy. The study involves two common dimensionality-reduction models, PCA and t-SNE. The correlation is stronger for PCA than for t-SNE in the current task design, a pattern consistent with the possibility that VA effectiveness is more closely associated with model literacy when model outputs are less directly readable from the visualization. Completion-time evidence does not show a stable efficiency gain, suggesting that differences in model intuitiveness may help explain when model knowledge shortens task completion and when it involves additional interpretive effort. The findings of this study suggest ways to further enrich VA evaluation methods and provide directions for developing more rigorous model-literacy assessment instruments.

Problem

• visual analytics
• model literacy
• summative evaluation
• dimensionality-reduction models
• performance

Innovation

• model literacy
• visual analytics
• dimensionality reduction
• evaluation factor
• PCA