Demo Data from SIGIR 2026 / reviewers in the wild / expert
Ching (Jean) Liu
dblp:239/9245
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2018
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous 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
1 paper |
Learning and educational technologies · 67% Collaborative and social computing · 33% | |
| Computer graphics and multimedia
1 paper |
Visualization and visual analytics · 100% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Learning and educational technologies
concept mapping |
0.3 | 1 | 2018 | ConceptScape: Collaborative Concept Mapping for Video Learning · CHI 2018 |
Collaborative and social computing
crowdsourcing |
0.3 | 1 | 2018 | ConceptScape: Collaborative Concept Mapping for Video Learning · CHI 2018 |
Learning and educational technologies
video-based learning |
0.3 | 1 | 2018 | ConceptScape: Collaborative Concept Mapping for Video Learning · CHI 2018 |
Methods — techniques the papers use, named apart from their topics
user study · 0.7crowdsourcing · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | ConceptScape: Collaborative Concept Mapping for Video LearningabstractWhile video has become a widely adopted medium for online learning, existing video players provide limited support for navigation and learning. It is difficult to locate parts of the video that are linked to specific concepts. Also, most video players afford passive watching, thus making it difficult for learners with limited metacognitive skills to deeply engage with the content and reflect on their understanding. To support concept-driven navigation and comprehension of lecture videos, we present ConceptScape, a system that generates and presents a concept map for lecture videos. ConceptScape engages crowd workers to collaboratively generate a concept map by prompting them to externalize reflections on the video. We present two studies to show that (1) interactive concept maps can be useful tools for concept-based video navigation and comprehension, and (2) with ConceptScape, novice crowd workers can collaboratively generate complex concept maps that match the quality of those by experts. Ching (Jean) Liu, Juho Kim 0001, Hao-Chuan Wang |
CHI | 1 |