Large-Scale Analysis of Visualization Options in a Citizen Science Game

被引：1

作者：

Miller, Josh Aaron ^{[1
]}

Lee, Vivian ^{[1
]}

Cooper, Seth ^{[1
]}

El-Nasr, Magy Seif ^{[1
]}

机构：

[1] Northeastern Univ, Boston, MA 02115 USA

来源：

CHI PLAY'19: EXTENDED ABSTRACTS OF THE ANNUAL SYMPOSIUM ON COMPUTER-HUMAN INTERACTION IN PLAY | 2019年

基金：

美国国家科学基金会; 美国国家卫生研究院;

关键词：

citizen science game; visualization; expertise; USER;

D O I：

10.1145/3341215.3356274

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Visualization is a valuable tool in problem solving, especially for citizen science games. In this study, we analyze data from 36,351 unique players of the citizen science game Foldit over a period of 5 years to understand how their choice of visualization options are affected by expertise and problem type. We identified clusters of visualization options, and found differences in how experts and novices view puzzles and that experts differentially change their views based on puzzle type. These results can inform new design approaches to help both novice and expert players visualize novel problems, develop expertise, and problem solve.

引用

页码：535 / 542

页数：8

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