DOI: 10.1111/jcal.12933 ISSN: 0266-4909

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

Ho Young Yoon, Seokmin Kang, Sungyeun Kim
  • Computer Science Applications
  • Education



Research 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).


This 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.


Asynchronous 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.


The 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 Discussion

This 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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