Demo Data from EDBT 2026 / reviewers in the wild / expert
Young Ji Kim
dblp:30/6641
· DBLP profile ↗
4ranked-venue papers
2as first author
0since 2021 · last 2017
0000-0002-5769-3771ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 3 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 1 · 1 first-author
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
3 papers |
Collaborative and social computing · 86% Wearable and physiological sensing · 7% Games and playful interaction · 7% |
Topics — the 2 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Collaborative and social computing
collective intelligence |
0.8 | 3 | 2017 | What Makes a Strong Team?: Using Collective Intelligence to Predict Team Performance in League of Legends · CSCW 2017 Deep Structures of Collaboration: Physiological Correlates of Collective Intelligence and Group Satisfaction · CSCW 2017 Collective Intelligence in Computer-Mediated Collaboration Emerges in Different Contexts and Cultures · CHI 2015 |
Collaborative and social computing › computer-mediated communication
computer-mediated collaboration |
0.2 | 1 | 2015 | Collective Intelligence in Computer-Mediated Collaboration Emerges in Different Contexts and Cultures · CHI 2015 |
Methods — techniques the papers use, named apart from their topics
statistical analysis · 0.3physiological sensing · 0.3laboratory experiment · 0.3standardized online battery · 0.2cross-cultural study · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | Deep Structures of Collaboration: Physiological Correlates of Collective Intelligence and Group SatisfactionabstractCollective intelligence (CI), a group's capacity to perform a wide variety of tasks, is a key factor in successful collaboration. Group composition, particularly diversity and member social perceptiveness, are consistent predictors of CI, but we have limited knowledge about the mechanisms underlying their effects. To address this gap, we examine how physiological synchrony, as an indicator of coordination and rapport, relates to CI in computer-mediated teams, and if synchrony might serve as a mechanism explaining the effect of group composition on CI. We present results from a laboratory experiment where 60 dyads completed the Test of Collective Intelligence (TCI) together online and rated their group satisfaction, while wearing physiological sensors. We find that synchrony in facial expressions (indicative of shared experience) was associated with CI and synchrony in electrodermal activity (indicative of shared arousal) with group satisfaction. Furthermore, various forms of synchrony mediated the effect of member diversity and social perceptiveness on CI and group satisfaction. Our results have important implications for online collaborations and distributed teams. Prerna Chikersal, Maria Tomprou, Young Ji Kim, Anita Williams Woolley, Laura A. Dabbish |
CSCW | 3 |
| 2017 | What Makes a Strong Team?: Using Collective Intelligence to Predict Team Performance in League of LegendsabstractRecent research has demonstrated that (a) groups can be characterized by a collective intelligence (CI) factor that measures their ability to perform together on a wide range of different tasks, and (b) this factor can predict groups' performance on other tasks in the future. The current study examines whether these results translate into the world of teams in competitive online video games where self-organized, time-pressured, and intense collaboration occurs purely online. In this study of teams playing the online game League of Legends, we find that CI does, indeed, predict the competitive performance of teams controlling for the amount of time played as a team. We also find that CI is positively correlated with the presence of a female team member and with the team members' average social perceptiveness. Finally, unlike in prior studies, tacit coordination in this setting plays a larger role than verbal communication. Young Ji Kim, David Engel, Anita Williams Woolley, Jeffrey Yu-Ting Lin, Naomi McArthur, Thomas W. Malone |
CSCW | 1 |
| 2015 | Collective Intelligence in Computer-Mediated Collaboration Emerges in Different Contexts and CulturesabstractCollective intelligence (CI) is a property of groups that emerges from the coordination and collaboration of members and predicts group performance on a wide range of tasks. Previous studies of CI have been conducted with lab-based groups in the USA. We introduce a new standardized online battery to measure CI and demonstrate consistent emergence of a CI factor across three different studies despite broad differences in (a) communication media (face-to-face vs online), (b) group contexts (short-term ad hoc groups vs long-term groups) and (c) cultural settings (US, Germany, and Japan). In two of the studies, we also show that CI is correlated with a group's performance on more complex tasks. Consequently, the CI metric provides a generalizable performance measure for groups that is robust to broad changes in media, context, and culture, making it useful for testing the effects of general-purpose collaboration technologies intended to improve group performance. David Engel, Anita Williams Woolley, Ishani Aggarwal, Christopher F. Chabris, Masamichi Takahashi, Keiichi Nemoto, Carolin Kaiser, Young Ji Kim, Thomas W. Malone |
CHI | 8 |
| 2006 | A Personalized Recommendation System Based on PRML for E-Commerce
Young Ji Kim, Hyeon Jeong Mun, Yong Tae Woo |
SOFSEM | 1 |