Improving Comprehension of Measurements Using Concrete Re-expression Strategies
ACM Human Factors in Computing Systems (CHI) 2018
A text article with our automated concrete re-expression tools using two common strategies: adding familiar context (left) and reunitization (right). Both strategies provide more context for the measurements by comparing them to measurements of familiar objects.
Abstract
It can be difficult to understand physical measurements (e.g., 28 lb, 600 gallons) that appear in news stories, data reports, and other documents. We develop tools that automatically re-express unfamiliar measurements using the measurements of familiar objects. Our work makes three contributions: (1) we identify effectiveness criteria for objects used in concrete measurement re-expressions; (2) we operationalize these criteria in a scalable method for mining a large dataset of concrete familiar objects with their physical dimensions from Amazon and Wikipedia; and (3) we develop automated concrete re-expression tools that implement three common re-expression strategies (adding familiar context, reunitization and proportional analogy) as energy minimization algorithms. Crowd-sourced evaluations of our tools indicate that people find news articles with re-expressions more helpful and re-expressions help them to better estimate new measurements.
Citation
BibTeX
@inproceedings{concrete-reexpression-2018,
title = {Improving Comprehension of Measurements Using Concrete Re-Expression Strategies},
author = {Hullman, Jessica and Kim, Yea-Seul and Nguyen, Francis and Speers, Lauren and Agrawala, Maneesh},
year = 2018,
booktitle = {Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems},
publisher = {ACM},
series = {CHI '18},
pages = {1–12},
doi = {10.1145/3173574.3173608}
}