In this paper we give a preview of our system for automatically evaluating attention in the classroom. We demonstrate our current behaviour metrics and preliminary observations on how they reflect the reactions of people to the given lecture. We also introduce foundations of our hypothesis on peripheral awareness of students during lectures.
Body language is an essential source of information in everyday communication. Low signal-to-noise ratio prevents us from using it in the automatic processing of student behaviour, an obstacle that we are slowly overcoming with advanced statistical methods. Instead of profiling individual behaviour of students in the classroom, the idea is to compare students and connect the observed traits to different levels of attention. With the usage of novel techniques from the field of computer vision, we focus on features that can be automatically extracted with a system of cameras, by means of passive observation of the classroom population. We show parallels between our work and previous theories and formulate a new concept for measuring the level of attention based on synchronization of student body movement. We observed that 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 metacognition. The goal is to improve the teacher-student conversation and to propose techniques that can enable a shorter feedback loop of the teacher's performance compared to the current-day methods.
Body language is an essential source of information in everyday communication. Low signal-to-noise ratio prevents us from using it in the automatic processing of student behaviour, an obstacle that we are slowly overcoming with advanced statistical methods. Instead of profiling individual behaviour of students in the classroom, the idea is to compare students and connect the observed traits to different levels of attention. With the usage of novel techniques from the field of computer vision, we focus on features that can be automatically extracted with a system of cameras, by means of passive observation of the classroom population. We show parallels between our work and previous theories and formulate a new concept for measuring the level of attention based on synchronization of student body movement. We observed that 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 metacognition. The goal is to improve the teacher-student conversation and to propose techniques that can enable a shorter feedback loop of the teacher's performance compared to the current-day methods.
We present our efforts towards building an observational system for measuring classroom activity. The goal is to explore visual cues which can be acquired with a system of video cameras and automatically processed to enrich the teacher's perception of the audience. The paper will give a brief overview of our methodology, explored features, and current findings.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.