# A Computer-Vision Based Application for Student Behavior Monitoring in Classroom
## 1 Introduction
### Background  
Many researchers have commented on the behaviors that influence students' performances. [PERSON] and [PERSON] have pointed out that students who are distracted by watching videos, playing games, or texting while taking lecture notes on digital devices are far more likely to have their long-term memory affected. In this manner, the students perform more poorly in exams, even if short-term memory is not impacted [4]. People like to think they can multitask. But this is a myth. What people are doing when they say they are multitasking is constant task switching. Although switching costs may be relatively small, sometimes just a few tenths of a second per switch, they can add up to significant amounts when people repeatedly switch back and forth between tasks [5]. When we switch from one task to another task, the brain cannot continue and keep up with everything that it has just done. Therefore, there will be a delay as one's attention moves from one task to another. When the students pay more attention in class, there is a higher probability of better achievement, as stated in the book of [PERSON], [PERSON] [6]: \"[PERSON] (1942) reported positive correlations between the degree of attentiveness as measured by [PERSON]'s cues and student achievement.\" The evidence which shows that digital devices influence the attention of students in the classroom is shown in a study by [PERSON] [7]. It showed \"a belief among teachers that constant use of digital technology hampered their student's attention spans and ability to persevere in the face of challenging tasks.\" Additionally, a survey written in the study showed that 71% of teachers thought technology damaged students' attention. And 64% people who took another survey said that technology did \"more to distract students than to help them acadically.\" [PERSON]'s study also pointed out that students have also identified learning distractions caused by digital technology. [PERSON], [PERSON], and [PERSON] [8] found out that texting during class partially affected a student's ability to self-regulate his/her sustained attention to classroom learning. In an earlier study, [PERSON] and [PERSON] [6] noted college a student's ability to text and perform other tasks simultaneously during class might become a habit over time. Such habits may be defined as automatic behaviors triggered by minimum consciousness [9]. To keep track of the actions/behaviors of students, two potential approaches can be taken: surveys and quizzes. However, these two approaches are inconvenient, and lack objectiveness, since the people might not remember what they did exactly. With the development of the computer vision field, the work of recording and analyzing students' behaviors in the classroom in real-time is not an impossible thing at present. [PERSON] et al. believe that a systematic understanding of each learner's educational needs is required, and they prepared customized instructional strategies and customized content by collecting, analyzing, and systematizing learners' data [10]. Today, academic analytics is one of the actions that can be captured with real-time data-reporting and predictive modeling, which helps suggest likely outcomes from familiar patterns of behavior. The faculty might soon be able to use these data on behaviors as guides for course redesign and as evidence for implementing new assessments and lines of communication between instructors and students [11]. In particular, one of the possible reasons that make students do things other than pay attention to the lessons is poor lesson content during lecture time. Since then, from the data observed, the department of students might communicate to the lecturers to modify the content to be as suitable as possible for students. On the other hand, lecturers themselves redesign that content to let students interested in the lessons instead of neglecting them.