Jiaohao Xu

dblp:387/5897 · DBLP profile ↗
← Back
2ranked-venue papers
0as first author
2since 2021 · last 2026
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 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.

Computer graphics and multimedia
1 paper
Visualization and visual analytics · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computing education · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Visualization and visual analytics
belief elicitation
1.012026
The Effects of Belief Elicitation in Visual Data Analysis: A Longitudinal Classroom Study · IEEE Trans. Vis. Comput. Graph. 2026
Visualization and visual analytics › visual analytics
exploratory data analysis
1.012026
The Effects of Belief Elicitation in Visual Data Analysis: A Longitudinal Classroom Study · IEEE Trans. Vis. Comput. Graph. 2026

Methods — techniques the papers use, named apart from their topics

longitudinal study · 2.0controlled experiment · 2.0
YearPublicationVenuePosition
2026 Wayfinding cognitive load in metro stations: Insights from multimodal physiological monitoring and data-driven analysis
Bochen Cao, Alima Adalaiti, Shiwen Pan, Jiaohao Xu, Zhao-Hui Sun, Linjun Lu
Adv. Eng. Informatics5
2026 The Effects of Belief Elicitation in Visual Data Analysis: A Longitudinal Classroom Study
abstract
Recent studies have reported a shortcoming of visual exploratory data analysis (EDA) that can lead analysts to report spurious findings. These findings have prompted the advocacy of incorporating belief elicitation within the data analysis process. However, the results from these studies primarily drew from laboratory experiments, which can differ from real-world analysis contexts. In this article, we present outcomes from a longitudinal study with students enrolled in a university-level visual analytics course tackling the VAST Challenge. The students formed teams that were randomly assigned to the belief elicitation and non-belief elicitation conditions. Our study results indicate teams that underwent belief elicitation adopted an intentional approach, while teams in the non-belief elicitation condition reported greater diversity in findings, aligning with prior research. Surprisingly, teams from both conditions achieved equal success in solving the VAST Challenge, suggesting that analysts can incorporate belief elicitation strategically for different goals. We provide guidelines for incorporating belief elicitation into data analysis and teaching material for educators to include belief elicitation in visual analytics courses.
Edward W. He, Vanessa Bellotti, Alexandra Scott, Jiaohao Xu, Ashley Suh 0001, Jennifer Rogers, Remco Chang
IEEE Trans. Vis. Comput. Graph.4