Sleepers' Lag – Study on Motion and Attention

Authors

  • Mirko Raca CHILI Lab (Computer-Human Interaction Lab for Learning & Instruction) École polytechnique fédérale de Lausanne
  • Roland Tormey CAPE group (Teaching Support Centre) École polytechnique fédérale de Lausanne (EPFL)
  • Pierre Dillenbourg CHILI Lab (Computer-Human Interaction Lab for Learning & Instruction) École polytechnique fédérale de Lausanne

DOI:

https://doi.org/10.18608/jla.2016.32.12

Abstract


Human body-language is one of the richest and most obscure sources of information in inter-personal communication. In this paper we present our observations of student-to-student influence and measurements. We show parallels with previous theories and formulate a new concept for measuring the level of attention based on synchronization of student actions. We observed that the students with lower levels of attention are slower to react than focused students, a phenomenon we named “sleepers' lag”. This realization may give rise to novel measurements that can act as a technological support for teacher's meta-cognition. The end goal is to improve the teacher-student conversation and to enable a shorter feedback loop of teacher's performance compared to the current-day methods.

Author Biographies

Mirko Raca, CHILI Lab (Computer-Human Interaction Lab for Learning & Instruction) École polytechnique fédérale de Lausanne

PhD student

CHILI Lab

Department of Information and Communication (I&C)

Roland Tormey, CAPE group (Teaching Support Centre) École polytechnique fédérale de Lausanne (EPFL)

Coordinator of Teaching Support Centre,

EPFL

Pierre Dillenbourg, CHILI Lab (Computer-Human Interaction Lab for Learning & Instruction) École polytechnique fédérale de Lausanne

Full professor,

Director of Center for Digital EducationSleepers' Lag – Study on Motion and Attention

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Published

2016-09-17

How to Cite

Raca, M., Tormey, R., & Dillenbourg, P. (2016). Sleepers’ Lag – Study on Motion and Attention. Journal of Learning Analytics, 3(2), 239-260. https://doi.org/10.18608/jla.2016.32.12

Issue

Section

Special section: Multimodal learning analytics