GestureTeach: A gesture guided online teaching interactive model

Liu, Hongjun; Yao, Chao; Zhang, Yalan; Ban, Xiaojuan

doi:10.1002/cav.2218

Cited by 2 publications

(2 citation statements)

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“…Our study also underscores the significant potential of visual gestures in both online and offline settings (Gu et al, 2020;Wang et al, 2020). There are also emerging efforts to create automated gesture recognition technologies tailored for online teaching platforms (Liu et al, 2023). These developments hold promise for significantly aiding the training of both PTs and ITs in the foreseeable future.…”

Section: The Ratio Of Body Orientationmentioning

confidence: 58%

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A non‐verbal teaching behaviour analysis for improving pointing out gestures: The case of asynchronous video lecture analysis using deep learning

Yoon,

Kang,

Kim

2023

Computer Assisted Learning

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BackgroundResearch into enhancing the effectiveness of information delivery in asynchronous video lectures remains sparse. This study analyzes the nonverbal teaching behaviours in asynchronous online videos, drawing comparisons between pre‐service and in‐service teachers (ITs).ObjectivesThis research primarily aims to juxtapose the nonverbal teaching behaviours, such as arm extensions and body orientation, utilized by pre‐service teachers (PTs) and ITs within asynchronous online videos.MethodsAsynchronous video lectures from four pre‐service and four ITs across four diverse subject topics were scrutinized. Leveraging deep learning technology, teachers' poses during their instruction towards a video camera were quantified, with a particular focus on arm stretch range and body orientation in relation to the subject being taught.ResultsThe findings revealed that PTs were deficient in effectively employing pointing gestures. Their arm stretches and body orientation towards the board were not differentiated across subjects. Conversely, ITs demonstrated subject‐specific variations in their arm extension and body orientation, signalling their effective strategies for knowledge dissemination.Conclusions and DiscussionThis study emphasizes the importance of assessing nonverbal teaching behaviours in the development of effective instructional training. It accentuates the need for nonverbal communication and subject‐specific teaching strategy training in PTs. Future investigations could broaden their scope to include larger sample sizes and expanded subject areas to discern more comprehensive trends in nonverbal teaching behaviours.

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Section: The Ratio Of Body Orientationmentioning

confidence: 58%

“…As a result, the I-frame is the most suitable frame for accurately tracing changes in body posture and identifying gestures. Prior studies have employed I-frames as the baseline for human pose detection (Fan et al, 2021;Liu et al, 2023).…”

Section: Frame Extractionmentioning

confidence: 99%

A non‐verbal teaching behaviour analysis for improving pointing out gestures: The case of asynchronous video lecture analysis using deep learning

Yoon,

Kang,

Kim

2023

Computer Assisted Learning

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Continuous Recognition of Teachers’ Hand Signals for Students with Attention Deficits

Chen,

Yang,

et al. 2024

Algorithms

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In the era of inclusive education, students with attention deficits are integrated into the general classroom. To ensure a seamless transition of students’ focus towards the teacher’s instruction throughout the course and to align with the teaching pace, this paper proposes a continuous recognition algorithm for capturing teachers’ dynamic gesture signals. This algorithm aims to offer instructional attention cues for students with attention deficits. According to the body landmarks of the teacher’s skeleton by using vision and machine learning-based MediaPipe BlazePose, the proposed method uses simple rules to detect the teacher’s hand signals dynamically and provides three kinds of attention cues (Pointing to left, Pointing to right, and Non-pointing) during the class. Experimental results show the average accuracy, sensitivity, specificity, precision, and F1 score achieved 88.31%, 91.03%, 93.99%, 86.32%, and 88.03%, respectively. By analyzing non-verbal behavior, our method of competent performance can replace verbal reminders from the teacher and be helpful for students with attention deficits in inclusive education.

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GestureTeach: A gesture guided online teaching interactive model

Cited by 2 publications

References 30 publications

A non‐verbal teaching behaviour analysis for improving pointing out gestures: The case of asynchronous video lecture analysis using deep learning

A non‐verbal teaching behaviour analysis for improving pointing out gestures: The case of asynchronous video lecture analysis using deep learning

Continuous Recognition of Teachers’ Hand Signals for Students with Attention Deficits

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