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Oriented by the observation that cross-cultural comparisons of mathematics teaching have had little impact on the evolution of classroom practice, we develop the thesis that approaches to school mathematics are rooted in metaphors and sustained by associations that are language specific. We examined distinct webs of meaning for the words "learn" and "teach" in three languages: English, Czech, and Japanese. By applying a tool designed for analyzing bodily classroom (inter)activity, we aim to demonstrate that the meanings of these words are evident in practices that are readily observed in video recordings of mathematics lessons conducted in these languages. We suggest that efforts toward "transcreation" rather than "translation" are better fitted to hopes of interpreting and informing classroom practices across languages. [ABSTRACT FROM AUTHOR]

Hand-raising gesture is one of the most popular signs of communication, whose frequency is related to the classroom’s atmosphere, the attractiveness of the subject, and the level of interactions between students and teachers. However, automatic hand-raising gesture detection and recognition remains still a challenging problem mainly due to low hand resolution, hand occlusion, various backgrounds, and viewpoint changes. While majority of the existing methods focus on static hand-raising posture detection, in this paper, we propose a framework for dynamic hand gesture recognition from classroom videos consisting of two main stages: hand posture detection and dynamic hand gesture recognition. In hand posture detection stage, we extend the previous work by adding relative position-aware in non-local network. After detecting the hand-raising posture on static pixels, which in turn accelerates the performance images, at the dynamic hand gesture recognition stage, we incorporate object detection and tracking to associate the hand-raising detection results at consecutive frames to supplement the missing detection due to the occlusion issue and obtain hand-raising gesture recognition at the event level. The experimental results show that the proposed method outperforms three benchmark models for static hand posture detection that are Faster-RCNN, Libra-RCNN, and Libra-RCNN+RDA on our dataset with the gains obtained for mAP being 7.68%, 5.76%, and 0.35% higher, respectively. In terms of event-level recognition results, the proposed method achieves the value of frame-wise accuracy, temporal_IoU, F1-score@0.3, and Levenshtein-score are 90.0%, 84.4%, 83.2%, and 84.3%, respectively. The code and dataset used in the paper will be made publicity available for research community.

Hand-raising gesture is one of the most popular signs of communication, whose frequency is related to the classroom's atmosphere, the attractiveness of the subject, and the level of interactions between students and teachers. However, automatic hand-raising gesture detection and recognition remains still a challenging problem mainly due to low hand resolution, hand occlusion, various backgrounds, and viewpoint changes. While majority of the existing methods focus on static hand-raising posture detection, in this paper, we propose a framework for dynamic hand gesture recognition from classroom videos consisting of two main stages: hand posture detection and dynamic hand gesture recognition. In hand posture detection stage, we extend the previous work by adding relative position-aware in non-local network. After detecting the hand-raising posture on static pixels, which in turn accelerates the performance images, at the dynamic hand gesture recognition stage, we incorporate object detection and tracking to associate the hand-raising detection results at consecutive frames to supplement the missing detection due to the occlusion issue and obtain hand-raising gesture recognition at the event level. The experimental results show that the proposed method outperforms three benchmark models for static hand posture detection that are Faster-RCNN, Libra-RCNN, and Libra-RCNN+RDA on our dataset with the gains obtained for mAP being 7.68%, 5.76%, and 0.35% higher, respectively. In terms of event-level recognition results, the proposed method achieves the value of frame-wise accuracy, temporal_IoU, F1-score@0.3, and Levenshtein-score are 90.0%, 84.4%, 83.2%, and 84.3%, respectively. The code and dataset used in the paper will be made publicity available for research community. [ABSTRACT FROM AUTHOR]

Abstract This article presents multiple episodes drawing from three distinct research projects conducted in multilingual classrooms in Luxembourg, to underscore the value of video analysis in culturally and linguistically diverse classroom contexts. We show how video analysis that valorizes the non-verbal in interaction has the ability to reveal communicative resources often masked by analysis rooted in the verbal. From the examples presented, that span teacher and student interactions in both elementary and secondary classrooms, we make a methodological argument based on analytical approaches utilized in all three research projects to demonstrate how we have come to an expanded notion of voice in our research that is revealed through multimodal video analysis. Specific analytical approaches that illuminate the embodied and multimodal aspects of voice are discussed. We conclude by underscoring the benefits of embodied and multimodal approaches to video analysis for research with all students, but most importantly for students often marginalized through analytical approaches that prioritize the verbal. Finally, we discuss the implications of video research that works to highlight resource-rich views of teaching and learning across learning contexts.

This article presents multiple episodes drawing from three distinct research projects conducted in multilingual classrooms in Luxembourg, to underscore the value of video analysis in culturally and linguistically diverse classroom contexts. We show how video analysis that valorizes the non-verbal in interaction has the ability to reveal communicative resources often masked by analysis rooted in the verbal. From the examples presented, that span teacher and student interactions in both elementary and secondary classrooms, we make a methodological argument based on analytical approaches utilized in all three research projects to demonstrate how we have come to an expanded notion of voice in our research that is revealed through multimodal video analysis. Specific analytical approaches that illuminate the embodied and multimodal aspects of voice are discussed. We conclude by underscoring the benefits of embodied and multimodal approaches to video analysis for research with all students, but most importantly for students often marginalized through analytical approaches that prioritize the verbal. Finally, we discuss the implications of video research that works to highlight resource-rich views of teaching and learning across learning contexts. [ABSTRACT FROM AUTHOR]