# A Computer-Vision Based Application for Student Behavior Monitoring in Classroom
## 5 Discussion and Conclusions  
This research aimed to build a system that automatically supports teachers and related educational faculties with monitoring student behavior. We focused on the observation targets of the students across time. The system works as an assistant for the decision-making process. The strategic information may be discovered and delivered to the decision-makers automatically. We accomplished the building of an entire system that supports recording student behaviors, proceeding statistics, and visualizing the data. We provided the details of development and experiments and show the feasibility of combining model techniques to solve the student-behavior-tracking puzzle. Previous works have not taken advantage of deep learning for statistical analysis [12] or have only applied old computer vision models [13; 14]. We successfully applied the most recent, state-of-art deep learning models. Furthermore, a combination of those models and our methods for 3D coordinate estimation as well as gaze estimation was proposed to improve the performance. We applied the SSH face detector for the face detection module, that is, basically a combination of O-Net and L-Net (MTCNN), for facial landmark detection; Arcface for facial representation; and Hopenet for gaze  
Figure 8: (**A**) The pie chart shows the numerical proportion of gaze direction data during the class; (**B**) the area chart represents cumulated total number of appearances of gaze classes over time. estimation. For the face classification task, different learning algorithms were implemented. Instead of requiring specific devices and being restricted by their limitations [15], our outcome succeeded in dealing with a more realistic context. Although there was a limited range of behaviors detected compared with the ones in [16], we concentrated on the real educational environment, in which classrooms have a wider range of recording devices and greater number of students. Since the student behavior monitoring problem is bonded with many strict and tight requirements, there is a need for more investigation. Our first limitation includes the lack of monitoring of other useful information, such as emotions. More behavior-detection methods, such as facial expression, body pose, etc., are very suitable for the next improvement of the system. Another issue that we want to investigate more clearly, is the level of correlation between behaviors and the outcomes of students. This system could be utilized as the basis for further studies about those correlations in different environments. Our graphs displayed on the web application were said to be difficult to use for non-technical users. We are conducting a search for more suitable data visualization techniques. Besides, the current architecture requires a high-cost processing system. This is one of the barriers in the road to production. We need to build a better platform to reduce usage and maintenance costs. Conceptualization, B.N.A. and N.T.S. ; methodology, B.N.A. and N.T.S. ; software, N.H.T. ([PERSON]), N.C.D. and N.H.T. ([PERSON]); validation, B.N.A., N.T.S., P.T.L., L.P.C. and M.U.A. ; formal analysis, B.N.A. and N.T.S. ; investigation, N.H.T. ([PERSON]), N.C.D., N.H.T. ([PERSON]) and N.T.S. ; resources, L.P.C. ; data curation, B.N.A., N.T.S., N.H.T. ([PERSON]), N.Q.D. and N.H.T. ([PERSON]); writing--B.N.A., N.T.S., N.H.T. ([PERSON]), N.C.D. and N.H.T. ([PERSON]); writing--M.U.A. and T.V.D. and N.T.S. ; visualization, B.N.A. and N.T.S. ; supervision, B.N.A. and N.T.S. ; project administration, L.P.C. This research received no external funding. We would like to express our sincere thanks to the training department, computing fundamentals faculty members, and students in PRF192 Summer 2019 classes at FPT University. They facilitated, cooperated with, and assisted us in the data collection process. Without these contributions, we could not have completed this study. There authors declare no conflicts of interest.