Timeline-Anchored Comments in Video-Based Learning: The Impact of Visual Layout and Content Depth

Chen, Yue; Gao, Qin; Gao, Ge

doi:10.1080/10447318.2021.1976505

Cited by 9 publications

(1 citation statement)

References 88 publications

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“…Once participants viewed the video with signmaku, we asked for suggestions to enhance its effectiveness for better comprehension and learning. The draft version is similar to prior literature [20]. In that work, Chen et al found with the embedded layout, learners altered their attention more frequently between two comments and remembered more surface-level comments.…”

Section: Round 1: Needfinding Of Signmaku For Social Connectednesssupporting

confidence: 66%

Towards Inclusive Video Commenting: Introducing Signmaku for the Deaf and Hard-of-Hearing

Chen,

Cheng,

Situ

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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Previous research underscored the potential of danmaku-a textbased commenting feature on videos-in engaging hearing audiences. Yet, for many Deaf and hard-of-hearing (DHH) individuals, American Sign Language (ASL) takes precedence over English. To improve inclusivity, we introduce "Signmaku, " a new commenting mechanism that uses ASL, serving as a sign language counterpart to danmaku. Through a need-finding study (N=12) and a withinsubject experiment (N=20), we evaluated three design styles: real human faces, cartoon-like figures, and robotic representations. The results showed that cartoon-like signmaku not only entertained but also encouraged participants to create and share ASL comments, with fewer privacy concerns compared to the other designs. Conversely, the robotic representations faced challenges in accurately depicting hand movements and facial expressions, resulting in higher cognitive demands on users. Signmaku featuring real human faces elicited the lowest cognitive load and was the most comprehensible among all three types. Our findings offered novel design implications for leveraging generative AI to create signmaku comments, enriching co-learning experiences for DHH individuals.

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Section: Round 1: Needfinding Of Signmaku For Social Connectednesssupporting

confidence: 66%

Towards Inclusive Video Commenting: Introducing Signmaku for the Deaf and Hard-of-Hearing

Chen,

Cheng,

Situ

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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On enhancing students’ cognitive abilities in online learning using brain activity and eye movements

2022

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The COVID-19 pandemic has interrupted education institutions in over 150 nations, affecting billions of students. Many governments have forced a transition in higher education from in-person to remote learning. After this abrupt, worldwide transition away from the classroom, some question whether online education will continue to grow in acceptance in post-pandemic times. However, new technology, such as the brain-computer interface and eye-tracking, have the potential to improve the remote learning environment, which currently faces several obstacles and deficiencies. Cognitive brain computer interfaces can help us develop a better understanding of brain functions, allowing for the development of more effective learning methodologies and the enhancement of brain-based skills. We carried out a systematic literature review of research on the use of brain computer interfaces and eye-tracking to measure students’ cognitive skills during online learning. We found that, because many experimental tasks depend on recorded rather than real-time video, students don’t have direct and real-time interaction with their teacher. Further, we found no evidence in any of the reviewed papers for brain-to-brain synchronization during remote learning. This points to a potentially fruitful future application of brain computer interfaces in education, investigating whether the brains of student-teacher pairs who interact with the same course content have increasingly similar brain patterns.

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A study on user Danmaku sending and emotional expression in online travel videos based on multimodal fusion

Luo,

Wang,

2024

Electron Commer Res

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Timeline-Anchored Comments in Video-Based Learning: The Impact of Visual Layout and Content Depth

Cited by 9 publications

References 88 publications

Towards Inclusive Video Commenting: Introducing Signmaku for the Deaf and Hard-of-Hearing

Towards Inclusive Video Commenting: Introducing Signmaku for the Deaf and Hard-of-Hearing

On enhancing students’ cognitive abilities in online learning using brain activity and eye movements

A study on user Danmaku sending and emotional expression in online travel videos based on multimodal fusion

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