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Ching (Jean) Liu

dblp:239/9245 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Learning and educational technologies
concept mapping
0.312018
ConceptScape: Collaborative Concept Mapping for Video Learning · CHI 2018
Collaborative and social computing
crowdsourcing
0.312018
ConceptScape: Collaborative Concept Mapping for Video Learning · CHI 2018
Learning and educational technologies
video-based learning
0.312018
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
YearPublicationVenuePosition
2018 ConceptScape: Collaborative Concept Mapping for Video Learning
abstract
While 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
CHI1