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Chen Chen 0109

dblp:65/4423-109 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2023
0000-0002-3216-0522ORCID · verified

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

Human-computer interaction and ubiquitous computing · 2 · 2 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.

Human-computer interaction and pervasive computing
1 paper
Collaborative and social computing · 56% Immersive interaction · 44%

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

TopicWeightPapersLastEvidence papers
Collaborative and social computing
collaborative learning
0.712023
Collaborative Online Learning with VR Video: Roles of Collaborative Tools and Shared Video Control · CHI 2023
Immersive interaction
virtual reality
0.712023
Collaborative Online Learning with VR Video: Roles of Collaborative Tools and Shared Video Control · CHI 2023
Collaborative and social computing › collaborative learning
computer-supported collaborative learning
0.212023
Collaborative Online Learning with VR Video: Roles of Collaborative Tools and Shared Video Control · CHI 2023

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

within-subject study · 0.7
YearPublicationVenuePosition
2023 Collaborative Online Learning with VR Video: Roles of Collaborative Tools and Shared Video Control
abstract
Virtual Reality (VR) has a noteworthy educational potential by providing immersive and collaborative environments. As an alternative but cost-effective way of delivering realistic environments in VR, using 360-degree videos in immersive VR (VR videos) received more attention. Although many studies reported positive learning experiences with VR videos, little is known about how collaborative learning performs on VR video viewing systems. In this study, we implemented two collaborative VR video viewing modes based on the way of group video control, synchronized or shared (Sync mode) and non-synchronized or individual (Non-sync mode) video control, against a conventional VR video viewing setting (Basic mode). We conducted a within-subject study (N = 54) in a lab-simulated remote learning environment. Our results show that collaborative VR video modes (Sync and Non-sync mode) improve users’ learning experiences and collaboration quality, especially with shared video control. Our findings provide directions for designing and employing collaborative VR video tools in online learning environments.
Qiao Jin 0002, Yu Liu 0096, Ruixuan Sun, Chen Chen 0109, Puqi Zhou, Bo Han 0001, Feng Qian 0001, Svetlana Yarosh
CHI4
2023 Getting the Most from Eye-Tracking: User-Interaction Based Reading Region Estimation Dataset and Models
abstract
A single digital newsletter usually contains many messages (regions). Users’ reading time spent on, and read level (skip/skim/read-in-detail) of each message is important for platforms to understand their users’ interests, personalize their contents, and make recommendations. Based on accurate but expensive-to-collect eyetracker-recorded data, we built models that predict per-region reading time based on easy-to-collect Javascript browser tracking data.
Ruoyan Kong, Ruixuan Sun, Charles Chuankai Zhang, Chen Chen 0109, Sneha Patri, Gayathri Gajjela, Joseph A. Konstan
ETRA4