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Explaining the Gap: Visualizing One's Predictions Improves Recall and Comprehension of Data

Yea-Seul Kim, Katharina Reinecke, Jessica Hullman. ACM Human Factors in Computing Systems (CHI), 2017
Figure for Explaining the Gap: Visualizing One's Predictions Improves Recall and Comprehension of Data
The study interface for experimental visual conditions.
Materials
PDF | Best Paper Award
Abstract
Information visualizations use interactivity to enable user-driven querying of visualized data. However, users’ interactions with their internal representations, including their expectations about data, are also critical for a visualization to support learning. We present multiple graphically-based techniques for eliciting and incorporating a user’s prior knowledge about data into visualization interaction. We use controlled experiments to evaluate how graphically eliciting forms of prior knowledge and presenting feedback on the gap between prior knowledge and the observed data impacts a user’s ability to recall and understand the data. We find that participants who are prompted to reflect on their prior knowledge by predicting and self-explaining data outperform a control group in recall and comprehension. These effects persist when participants have moderate or little prior knowledge on the datasets. We discuss how the effects differ based on text versus visual presentations of data. We characterize the design space of graphical prediction and feedback techniques and describe design recommendations.
BibTeX
@inproceedings{2017-explaining-the-gap,
  title = {Explaining the Gap: Visualizing One's Predictions Improves Recall and Comprehension of Data},
  author = {Kim, Yea-Seul AND Reinecke, Katharina AND Hullman, Jessica},
  booktitle = {ACM Human Factors in Computing Systems (CHI)},
  year = {2017},
  url = {https://idl.uw.edu/papers/explaining-the-gap},
  doi = {10.1145/3025453.3025592}
}