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Chapter: 'How to Attract Students’ Visual Attention' from book: Adaptive and Adaptable Learning: 11th European Conference on Technology Enhanced Learning, EC-TEL 2016, Lyon, France, September 13-16, 2016, Proceedings (Book chapter)

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Book Series: Lecture Notes in Computer Science ISSN: 0302-9743 / 1611-3349 ISBN: 9783319451527 9783319451534 Year: Pages: 11 DOI: 10.1007/978-3-319-45153-4_3 Language: English
Publisher: Springer
Subject: Education
Added to DOAB on : 2017-11-27 16:51:33
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Attracting students’ visual attention is critical in order for teachers to teach classes, communicate core concepts and emotionally connect with their students. In this paper we analyze two months of video recordings taken from a fourth grade class in a vulnerable school, where, every day, a sample of 3 students wore a mini video camera mounted on eyeglasses. We looked for scenes from the recordings where the teacher appears in the students’ visual field, and computed the average duration of each event. We found that the student’s gaze on the teacher lasted 44.9 % longer when the teacher gestured than when he did not, with an effect size (Cohen’s d) of 0.69. The data also reveals different effects for gender, subject matter, and student Grade Point Average (GPA). The effect of teacher gesturing on students with a low GPA is higher than on students’ with a high GPA. These findings may have broad significance for improving teaching practices.

Analysis and recognition of human actions with flow features and temporal models

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ISBN: 9783731502821 Year: Pages: XV, 183 p. DOI: 10.5445/KSP/1000043583 Language: ENGLISH
Publisher: KIT Scientific Publishing
Subject: Computer Science
Added to DOAB on : 2019-07-30 20:02:01
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This work focuses the recognition of complex human activities in video data. A combination of new features and techniques from speech recognition is used to realize a recognition of action units and their combinations in video sequences. The presented approach shows how motion information gained from video data can be used to interpret the underlying structural information of actions and how higher level models allow an abstraction of different motion categories beyond simple classification.

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