Demo Data from SIGIR 2026 / reviewers in the wild / expert
Richard E. Ferdig
dblp:64/5215 · also Rick Ferdig
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2014
0000-0001-6823-1663ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Human-computer interaction and pervasive computing
1 paper |
Human-AI interaction · 44% Health and well-being technologies · 44% Usability and user experience research · 13% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Health and well-being technologies
health literacy |
0.2 | 1 | 2014 | Getting the Point Across: Exploring the Effects of Dynamic Virtual Humans in an Interactive Museum Exhibit on User Perceptions · IEEE Trans. Vis. Comput. Graph. 2014 |
Human-AI interaction › virtual agents
virtual human |
0.2 | 1 | 2014 | Getting the Point Across: Exploring the Effects of Dynamic Virtual Humans in an Interactive Museum Exhibit on User Perceptions · IEEE Trans. Vis. Comput. Graph. 2014 |
Usability and user experience research
user perception |
0.1 | 1 | 2014 | Getting the Point Across: Exploring the Effects of Dynamic Virtual Humans in an Interactive Museum Exhibit on User Perceptions · IEEE Trans. Vis. Comput. Graph. 2014 |
Methods — techniques the papers use, named apart from their topics
user study · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | Getting the Point Across: Exploring the Effects of Dynamic Virtual Humans in an Interactive Museum Exhibit on User PerceptionsabstractWe have created “You, M.D.”, an interactive museum exhibit in which users learn about topics in public health literacy while interacting with virtual humans. You, M.D. is equipped with a weight sensor, a height sensor and a Microsoft Kinect that gather basic user information. Conceptually, You, M.D. could use this user information to dynamically select the appearance of the virtual humans in the interaction attempting to improve learning outcomes and user perception for each particular user. For this concept to be possible, a better understanding of how different elements of the visual appearance of a virtual human affects user perceptions is required. In this paper, we present the results of an initial user study with a large sample size (n =333) ran using You, M.D. The study measured users’ reactions based on the user’s gender and body-mass index (BMI) when facing virtual humans with BMI either concordant or discordant from the user’s BMI. The results of the study indicate that concordance between the users’ BMI and the virtual human’s BMI affects male and female users differently. The results also show that female users rate virtual humans as more knowledgeable than male users rate the same virtual humans. Diego J. Rivera-Gutierrez, Richard E. Ferdig, Benjamin Lok |
IEEE Trans. Vis. Comput. Graph. | 2 |