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Comparing and Combining Interaction Data and Eye-tracking Data for the Real-time Prediction of User Cognitive Abilities in Visualization Tasks
Previous work has shown that some user cognitive abilities relevant for processing information visualizations can be predicted from eye-tracking data. Performing this type of user modeling is important for devising visualizations that can detect a user'...
PolicyFlow: Interpreting Policy Diffusion in Context
Stability in social, technical, and financial systems, as well as the capacity of organizations to work across borders, requires consistency in public policy across jurisdictions. The diffusion of laws and regulations across political boundaries can ...
Designing an AI Health Coach and Studying Its Utility in Promoting Regular Aerobic Exercise
Our research aims to develop interactive, social agents that can coach people to learn new tasks, skills, and habits. In this article, we focus on coaching sedentary, overweight individuals (i.e., “trainees”) to exercise regularly. We employ adaptive ...
Mental Models of Mere Mortals with Explanations of Reinforcement Learning
- Andrew Anderson,
- Jonathan Dodge,
- Amrita Sadarangani,
- Zoe Juozapaitis,
- Evan Newman,
- Jed Irvine,
- Souti Chattopadhyay,
- Matthew Olson,
- Alan Fern,
- Margaret Burnett
How should reinforcement learning (RL) agents explain themselves to humans not trained in AI? To gain insights into this question, we conducted a 124-participant, four-treatment experiment to compare participants’ mental models of an RL agent in the ...
Automatic Detection of Usability Problem Encounters in Think-aloud Sessions
Think-aloud protocols are a highly valued usability testing method for identifying usability problems. Despite the value of conducting think-aloud usability test sessions, analyzing think-aloud sessions is often time-consuming and labor-intensive. ...