October 2022 | IEEE Visualization and Visual Analytics (VIS)

VisQuiz: Exploring Feedback Mechanisms to Improve Graphical Perception

Ryan Birchfield, Maddison Caten, Errica Cheng, Madyson Kelly, Truman Larson, Hoang Phan Pham, Yiren Ding, Noëlle Rakotondravony, Lane T. Harrison

Paper

Example task from Stage 2 in the feedback condition showing feedback given after an answer is submitted. The greyed area illustrates a frame of the animation before the final result (right) where the orange circle transitions onto the blue one, contrasting the participant’s answer with the correct answer.

Abstract

In this paper, we explore the design and evaluation of feedback for graphical perception tasks, called VisQuiz. Using a quiz-like metaphor, we design feedback for a typical visualization comparison experiment, showing participants their answer alongside the correct answer in an animated sequence in each trial, as well as summary feedback at the end of trial sections. To evaluate VisQuiz, we conduct a between-subjects experiment, including three stages of 40 trials each with a control condition that included only summary feedback. Results from n = 80 participants show that once participants started receiving trial feedback (Stage 2) they performed significantly better with bubble charts than those in the control condition. This effect carried over when feedback was removed (Stage 3). Results also suggest an overall trend of improved performance due to feedback. We discuss these findings in the context of other visualization literacy efforts, and possible future work at the intersection of visualization, feedback, and learning. Experiment data and analysis scripts are available at the following repository https://osf.io/jys5d/

BibTeX

@inproceedings{birchfield2022visquiz,
  title={VisQuiz: Exploring Feedback Mechanisms to Improve Graphical Perception},
  author={Birchfield, Ryan and Caten, Maddison and Cheng, Errica and Kelly, Madyson and Larson, Truman and Pham, Hoan Phan and Ding, Yiren and Rakotondravony, No{\"e}lle and Harrison, Lane},
  booktitle={2022 IEEE Visualization and Visual Analytics (VIS)},
  pages={95--99},
  year={2022},
  organization={IEEE},
  url         = {https://doi.org/10.48550/arXiv.2207.09534},
  doi         = {10.1109/VIS54862.2022.00028},
}