# A Computer-Vision Based Application for Student Behavior Monitoring in Classroom
## 4 Results and Discussion
Figure 6: Confusion matrix of ID identification task: no assist (left) and semi-assist (right). Figure 7: (**A**) The confusion matrix of row seat estimation. (**B**) The confusion matrix of column seat estimation. For the result-visualization task, combined with gaze classification, we divide our dataset into four classes: board (student gazes at the board), laptop (student gazes at the laptop), other (student gazes at the others), and undetected (student is undetected). In order to make the data visualization task clearer and more understandable, we propose two types of charts--a pie chart and an area chart, as in Figure 8. The pie chart (Figure 8A) illustrates the numerical proportion of gaze direction data during the class. The arc length of each slice (or its central angle and area) is proportional to the quantity of corresponding class it presents. From the chart, we can see that the \"other\" gaze class has the greatest quantity, while the class of \"board\" takes the least amount. The percentages of \"laptop\" and \"undetected\" classes are quite similar. The area chart (Figure 8B) represents cumulated total number of appearances of gaze classes over time. With the chart, we are able to observe how just one quantity of class changes, or it will show us the changes in many quantities over time. Concretely, we divide each lecture duration into time windows, as horizontal axes (e.g., 211 windows in the above area chart). On each time window, we proceed to take a fixed number of gaze evaluations. In particular, there are 100 gaze evaluations on each window in the chart 8B, corresponding with 100 total appearances of gaze classes, as the vertical axis. On the window 100 (with 0-index), for example, the number in the \"board\" class is approximately 50; it is about 40 for \"laptop\"; the amount is over 10 for the \"other\" gaze class; and for class \"undetected,\" there is no recorded result. Just a quick glance at the aggregated results from the web app teacher can capture the overall situation of each individual student in the classroom, which they can hardly achieve by a manual process when the number of students is large. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Mean Absolute Error** \\ \hline Row seat estimation & 0.3876915803 \\ Column seat estimation & 0.0003939955 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mean absolute error (MAE) of position estimation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **SVM** & \begin{tabular}{c} **Decision** \\ **Tree** \\ \end{tabular} & \
\begin{tabular}{c} **Gradient Boosting (100** \\ **Random States)** \\ \end{tabular} &
\begin{tabular}{c} **Random Forest** \\ **(100 Estimators)** \\ \end{tabular} \\ \hline Original & 0.9061 & 0.9000 & 0.8644 & 0.9282 \\ SMOTE & 0.9046 & 0.8956 & 0.8837 & 0.9333 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Gaze classification accuracy table. Figure 8: \(Cont.\)  
The data collected from the system brings information about the attention of identified learners. For different subjects and curriculums, or different type of users, the data bring valuable information from different aspects depending on how they are used. For example, in the theoretical classes of the data shows that identified students were less focused on the board/slide than their classmates. This is most likely an indication of a lack of concentration during class. The teacher could take action to bring each student's concentration back. In our institution, we have two types of ICT classes: lab and theory, with the detailed plan of implementation for the course already declared as a part of teaching material. The training department and quality assurance may link the student behavior observed with the plan of implementation to consider whether the course was implemented as planned (in the theoretical class, the student may focus on the board more. Meanwhile, in the practical class, they may focus on their laptops more.