Students Activity Recognition by Heart Rate Monitoring in Classroom using K-Means Classification

Hadi Helmi Md Zuraini, W. Ismail, R. Hendradi, Army Justitia
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引用次数: 3

Abstract

Background : Heartbeat playing the main roles in our life. With the heartbeat, the anxiety level can be known. Most of the heartbeat is used in the exercise. Heart rate measurement is unique and uncontrollable by any human being. Objective: This research aims to learn student’s actions by monitoring the heart rate. In this paper, we are measuring the student reaction and action in classroom can give impact on teacher’s way of delivery when in the teaching session. In monitoring, student’s behavior may give feedback whether the teaching session have positive or negative outcome. Methods: The method we use is K-Means algorithm. Firstly, we need to know the student’s normal heartbeat as benchmark. We used Hexiware for collecting data from students’ hear beat. We perform the classification where K is benchmark students’ heartbeat. K-Means algorithm performs classification of the heart rate measurement of students. Results: We did the testing for five students in different subjects. It shows that all students have anxiety during the testing and presentation. Its consistency because we tested 5 students with mixes activities in the classroom, where the student has quiz, presentation and only teaching. Conclusion: Heart rate during studying in the classroom can change the education world in improving the efficiency of knowledge transfer between student and teacher. This research may act as basic way in monitoring student behavior in the classroom. We have tested for 5 students. Three students have their anxiety in classroom during the exam, presentation, and question. Two students have normal rate during the seminar and lecturer. The drawback, Hexiware is capturing average of ten minutes and tested in different classes and students. In future, we need just measure one student for all the subjects and Hexiware need to configure in one minute.
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基于k -均值分类的课堂心率监测中学生活动识别
背景:心跳在我们的生活中扮演着重要的角色。通过心跳,可以知道焦虑程度。大部分心跳都用在了运动中。心率测量是独一无二的,任何人都无法控制。目的:本研究旨在通过监测学生的心率来了解学生的行为。在本文中,我们测量了学生在课堂上的反应和行动在教学过程中对教师的教学方式的影响。在监控中,学生的行为可以给予反馈,无论教学过程的结果是积极的还是消极的。方法:采用K-Means算法。首先,我们需要知道学生的正常心跳作为基准。我们使用Hexiware收集学生的心跳数据。我们进行分类,其中K是基准学生的心跳。K-Means算法对学生的心率测量值进行分类。结果:我们对五名不同学科的学生进行了测试。这表明所有的学生在测试和演示过程中都有焦虑。它的一致性是因为我们测试了5名学生,在课堂上进行混合活动,学生有测验,演讲和教学。结论:课堂学习时的心率可以改变教育界,提高师生之间知识传递的效率。本研究可作为监控学生课堂行为的基本方法。我们测试了5个学生。三名学生在课堂上的焦虑表现在考试、陈述和提问。两名学生在研讨会和讲师的演讲中表现正常。缺点是,Hexiware平均占用10分钟的时间,并在不同的班级和学生中进行测试。未来,我们只需要测量一个学生的所有科目和Hexiware需要在一分钟内配置。
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