Demo Data from SIGIR 2026 / reviewers in the wild / expert
Elif Celik
dblp:339/9485
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 since 2021
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 |
Learning and educational technologies · 77% Usability and user experience research · 23% | |
| Computer graphics and multimedia
1 paper |
Virtual and augmented reality · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Virtual and augmented reality
immersive interaction |
0.8 | 1 | 2024 | Eye-Hand Coordination Training: A Systematic Comparison of 2D, VR, and AR Display Technologies and Task Instructions · VR 2024 |
Learning and educational technologies › skill acquisition › motor skill learning
eye-hand coordination training |
0.8 | 1 | 2024 | Eye-Hand Coordination Training: A Systematic Comparison of 2D, VR, and AR Display Technologies and Task Instructions · VR 2024 |
Methods — techniques the papers use, named apart from their topics
throughput analysis · 1.5longitudinal user study · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Eye-Hand Coordination Training: A Systematic Comparison of 2D, VR, and AR Display Technologies and Task InstructionsabstractPrevious studies on Eye-Hand Coordination Training (EHCT) focused on the comparison of user motor performance across different hardware with cross-sectional studies. In this paper, we compare user motor performance with an EHCT setup in Augmented Reality (AR), Virtual Reality (VR), and on a 2D touchscreen display in a longitudinal study. Through a ten-day user study, we thoroughly analyzed the motor performance of twenty participants with five task instructions focusing on speed, error rate, accuracy, precision, and none. As a novel evaluation criterion, we also analyzed the participants’ performance in terms of effective throughput. The results showed that each task instruction has a different effect on one or more psychomotor characteristics of the trainee, which highlights the importance of personalized training programs. Regarding different display technologies, the majority of participants could see more improvement in VR than in 2D or AR. We also identified that effective throughput is a good candidate for monitoring overall motor performance progress in EHCT systems. Aliza Aliza, Irene Zaugg, Elif Celik, Wolfgang Stuerzlinger, Francisco R. Ortega 0001, Anil Ufuk Batmaz, Mine Sarac |
VR | 3 |