MeetScript: Designing Transcript-based Interactions to Support Active Participation in Group Video Meetings
September 21, 2023 ยท Declared Dead ยท ๐ Proc. ACM Hum. Comput. Interact.
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Xinyue Chen, Shuo Li, Shipeng Liu, Robin Fowler, Xu Wang
arXiv ID
2309.12115
Category
cs.HC: Human-Computer Interaction
Citations
25
Venue
Proc. ACM Hum. Comput. Interact.
Last Checked
3 months ago
Abstract
While videoconferencing is prevalent, concurrent participation channels are limited. People experience challenges keeping up with the discussion, and misunderstanding frequently occurs. Through a formative study, we probed into the design space of providing real-time transcripts as an extra communication space for video meeting attendees. We then present MeetScript, a system that provides parallel participation channels through real-time interactive transcripts. MeetScript visualizes the discussion through a chat-alike interface and allows meeting attendees to make real-time collaborative annotations. Over time, MeetScript gradually hides extraneous content to retain the most essential information on the transcript, with the goal of reducing the cognitive load required on users to process the information in real time. In an experiment with 80 users in 22 teams, we compared MeetScript with two baseline conditions where participants used Zoom alone (business-as-usual), or Zoom with an adds-on transcription service (Otter.ai). We found that MeetScript significantly enhanced people's non-verbal participation and recollection of their teams' decision-making processes compared to the baselines. Users liked that MeetScript allowed them to easily navigate the transcript and contextualize feedback and new ideas with existing ones.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Human-Computer Interaction
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
๐ป
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
๐ป
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
๐ป
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
๐ป
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Language Models are Few-Shot Learners
R.I.P.
๐ป
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
๐ป
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
๐ป
Ghosted