Online Teaching Gestures Recognition Model Based on Deep Learning

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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ST-TGR: Spatio-Temporal Representation Learning for Skeleton-Based Teaching Gesture Recognition

Chen,

Huang,

Liu

et al. 2024

Sensors

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Teaching gesture recognition is a technique used to recognize the hand movements of teachers in classroom teaching scenarios. This technology is widely used in education, including for classroom teaching evaluation, enhancing online teaching, and assisting special education. However, current research on gesture recognition in teaching mainly focuses on detecting the static gestures of individual students and analyzing their classroom behavior. To analyze the teacher’s gestures and mitigate the difficulty of single-target dynamic gesture recognition in multi-person teaching scenarios, this paper proposes skeleton-based teaching gesture recognition (ST-TGR), which learns through spatio-temporal representation. This method mainly uses the human pose estimation technique RTMPose to extract the coordinates of the keypoints of the teacher’s skeleton and then inputs the recognized sequence of the teacher’s skeleton into the MoGRU action recognition network for classifying gesture actions. The MoGRU action recognition module mainly learns the spatio-temporal representation of target actions by stacking a multi-scale bidirectional gated recurrent unit (BiGRU) and using improved attention mechanism modules. To validate the generalization of the action recognition network model, we conducted comparative experiments on datasets including NTU RGB+D 60, UT-Kinect Action3D, SBU Kinect Interaction, and Florence 3D. The results indicate that, compared with most existing baseline models, the model proposed in this article exhibits better performance in recognition accuracy and speed.

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Online Teaching Gestures Recognition Model Based on Deep Learning

Cited by 3 publications

References 3 publications

Research on Behavior Analysis of Real-Time Online Teaching for College Students Based on Head Gesture Recognition

Research on Behavior Analysis of Real-Time Online Teaching for College Students Based on Head Gesture Recognition

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

ST-TGR: Spatio-Temporal Representation Learning for Skeleton-Based Teaching Gesture Recognition

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