## 4 Experimental Setup
### Raw Data of Mouse Dynamics Collected for Static Questions  
In this scenario, the designed exams include static questions, which are MCQ or true & false, or both questions. The exams are performed on four different collections of MCQ and true & false questions. To cover all types of exam questions, the mouse movement behavior of 50 students answering static questions without cheating and with cheating is collected. As shown in  
Figure 8: A histogram of raw mouse movements showing the difference in the frequency of movements is as follows: (**a**) a KDE plot of question 3 answered by students without cheating; (**b**) a KDE plot of question 3 answered by students who are allowed to cheat. Figure 7: 2-d KDE plot showing the density distribution of raw mouse cursor movements of students as follows: (**a**) a KDE plot of question 3 answered by students without cheating; (**b**) a KDE plot of question 3 answered by students who are allowed to cheat. Figures 9-12, samples of mouse dynamic movement of two different collections of MCQ and true & false questions answered by students, which are questions 5 and 8 represented by the Histogram and kernel density estimate (KDE). Figure 10: A histogram of raw mouse movements showing the difference in the frequency of movements is as follows: (a) a KDE plot of question 5 answered by students without cheating; (b) a KDE plot of question 5 answered by students who are allowed to cheat. Figure 9: 2-d KDE plot showing the density distribution of raw mouse cursor movements of students as follows: (a) a KDE plot of question 5 answered by students without cheating; (b) a KDE plot of question 5 answered by students who are allowed to cheat. As presented in Figure 9a, when the students answered an exam containing several MCQ questions, almost all the mouse movements were in one place on the screen. On the other side, when the students cheat while answering the questions, the mouse movement behavior is across the whole screen as the students search through the internet for the solution to questions. As a consequence, they performed a lot of movement across the screen, as shown in Figure 9b. Figure 11: 2-d KDE plot showing the density distribution of raw mouse cursor movements of students as follows: (**a**) a KDE plot of question 8 answered by students without cheating; (**b**) a KDE plot of question 8 answered by students who are allowed to cheat. Figure 12: A histogram of raw mouse movements showing the difference in the frequency of movements is as follows: (**a**) a KDE plot of question 8 answered by students without cheating; (**b**) a KDE plot of question 8 answered by students who are allowed to cheat. Moreover, Figure 10 shows that students cheating while solving the questions performed more mouse movements than the students who were not cheating. For this reason, mouse dynamic movement can distinguish between cheating and non-cheating students. As presented in Figures 11 and 12, another sample of static questions, which includes true and false questions, is tested within the exam, and the raw data of mouse dynamics is collected. As shown in Figure 11a, the students performed less mouse movement when they answered the true and false questions honestly. However, when the students are allowed to cheat, they perform more mouse movements, as presented in Figure 11b. The results presented in Figure 12 show that as the students cheat, they perform more mouse movements than not cheating. As shown in Figures 5-12, the raw data of mouse dynamics for a sample of VRL-based questions and static questions are collected. These raw data showed that the cheating students could be distinguished from not cheating students from their mouse behavior for both static and VRL-based questions. As a consequence, the mouse behavior is sufficient to detect the cheating students with the least cost for any type of exam performed. As shown in Figures 5-12, the collected raw data of mouse movement varies for different questions.