Estimation of Student Classroom Attention Using a Novel Measure of Head Motion Coherence

  • Naoyuki SatoEmail author
  • Atsuko Tominaga
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11306)


Video-based head motion analysis has often been used to estimate student attention in the classroom. However, individual head motions variously depend on semantic events in the classroom (e.g., lecture slides), making it difficult to stably estimate student attention. In this article, we propose an index of students’ attention in the classroom based on head motion coherence among students. We evaluated this index using 40 students’ data recorded during a series of four classes. Results indicated that both head motion coherence and amplitude depended on the type of classroom activity the students were engaged in (e.g., lecture, individual, or group work) while motion coherence at an individual level was stable across the series of classes. These results suggest that head motion coherence captures elements of students’ attention and it may also reflect the role of long-term, individual features (e.g., personality and motivation) in attention.


Educational technology Video motion analysis Interpersonal synchronization Neuroeducation 



This work was supported by JSPS KAKENHI Grant Number 26540069.


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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Complex and Intelligent Systems, School of Systems Information ScienceFuture University HakodateHakodateJapan
  2. 2.Center for Meta Learning, School of Systems Information ScienceFuture University HakodateHakodateJapan

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