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Richard E. Ferdig

dblp:64/5215 · also Rick Ferdig · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Health and well-being technologies
health literacy
0.212014
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
0.212014
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
0.112014
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
YearPublicationVenuePosition
2014 Getting the Point Across: Exploring the Effects of Dynamic Virtual Humans in an Interactive Museum Exhibit on User Perceptions
abstract
We 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