Demo Data from VLDB 2026 / reviewers in the wild / expert
Gyeongri Kim
dblp:344/9417
· DBLP profile ↗
2ranked-venue papers
1as first author
2since 2021 · last 2025
0000-0001-6950-7975ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 2 · 1 first-author · 2 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
2 papers |
Accessibility and assistive technology · 44% Learning and educational technologies · 25% Games and playful interaction · 25% |
Topics — the 3 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Learning and educational technologies › AI in education
AI literacy |
0.9 | 1 | 2025 | Playful design for AI Literacy: Creating inclusive learning and assessment opportunities · Int. J. Hum. Comput. Stud. 2025 |
Games and playful interaction
playful design |
0.9 | 1 | 2025 | Playful design for AI Literacy: Creating inclusive learning and assessment opportunities · Int. J. Hum. Comput. Stud. 2025 |
Accessibility and assistive technology › image accessibility
accessible data visualization |
0.7 | 1 | 2023 | "Explain What a Treemap is": Exploratory Investigation of Strategies for Explaining Unfamiliar Chart to Blind and Low Vision Users · CHI 2023 |
Methods — techniques the papers use, named apart from their topics
design · 0.9prototype system · 0.7exploratory study · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Playful design for AI Literacy: Creating inclusive learning and assessment opportunities
Yoon Jeon Kim, Gyeongri Kim, Andy Stoiber |
Int. J. Hum. Comput. Stud. | 2 |
| 2023 | "Explain What a Treemap is": Exploratory Investigation of Strategies for Explaining Unfamiliar Chart to Blind and Low Vision UsersabstractVisualization designers increasingly use diverse types of visualizations, but assistive technologies and education for blind and low vision people often focus on elementary chart types. We explore textual explanation as a more generalizable solution. We define three dimensions of explanation strategies based on education theories: comparing to a familiar chart type, describing how to draw one, and using a concrete example. We develop a prototype system that automatically generates text explanations from a given chart specification. We conduct an exploratory study with 24 legally blind people to observe both the effectiveness and the perceived effectiveness of the strategies. The findings include: description of visual appearance is overall more effective than instructions for drawing, effective strategies differ by each chart type and by each participant, and the user’s perceived effectiveness does not always lead to better performance. We demonstrate the feasibility of an explanation generation system and compile design considerations. Gyeongri Kim, Yea-Seul Kim |
CHI | 1 |