# Cheating Detection in Online Exams Using Deep Learning and Machine Learning
## 3 Results
### Analysis Findings for Scenario-2 Classification Models  
The findings of the analyses conducted with deep learning and machine learning algorithms for the binary classification of \"copied\" behavior and \"did not copy\" behavior in the study are given in Table 6. In Table 6, among the deep learning models, the 10-layer DNN model has the highest accuracy rate of 96.9%. This shows that this model is quite powerful in terms of correctly classifying unethical behaviors in online exams. DT and LR have lower accuracy rates (0.885 and 0.907). The 10-layer DNN and XGBoost models have the highest sensitivity (0.948 and 0.900), indicating that these models capture positive classes well. The 10-layer DNN and SVM models have the highest F1 scores (0.956 and 0.909). This indicates that they have balanced performance in terms of both precision and sensitivity. The SVM model has the highest specificity rate (0.988), indicating that the model discriminates negative classes very well. The 10-layer DNN model has the highest MCC value (0.921), indicating that it generally performs well-balanced classification. The 10-layer DNN model has the lowest error rate, 3.1%, indicating that this model is more reliable in terms of overall performance. As a result, the model proposed within the scope of the study, a 10-layer DNN model, shows the best performance in general. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Layer No** & **Layer Type** & **Number of Neurons** & **Activation Function** & **Dropout** & **Explanation** \\ \hline \multirow{3}{*}{1} & \multirow{3}{*}{Dense (Fully Connected)} & \multirow{3}{*}{128} & \multirow{3}{*}{ReLU} & \multirow{3}{*}{0.2} & The input layer is fully connected with 128 neurons; ReLU activation is used. Twenty percent of the neurons are randomly disabled to avoid over-learning with dropout. \\ \cline{1-1} \cline{5-6}  & & & & & The second layer works with 64 neurons, again using ReLU activation and preventing overlearning with Dropout. \\ \hline \multirow{3}{*}{3} & \multirow{3}{*}{Dense (Fully Connected)} & \multirow{3}{*}{32} & \multirow{3}{*}{ReLU} & \multirow{3}{*}{0.2} & The third layer works with 32 neurons. ReLU activation is used, and Dropout is applied at 20\%. \\ \cline{1-1} \cline{5-6}  & & & & & The fourth layer works with 16 neurons and uses ReLU activation. Dropout is not used in this layer. \\ \hline \multirow{3}{*}{5} & \multirow{3}{*}{Dense (Fully Connected)} & \multirow{3}{*}{1} & Activation None & \multirow{3}{*}{None} & Output layer. Since it is a regression problem, an activation function is not used. A single output is produced. \\ \cline{1-1} \cline{5-6}  & & & None & & used. A single output is produced. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Layers and parameters used in 5-layer DNN. Figure 2: Plot of actual and predicted values with the 5-layer DNN model K-10 cross-validation. The training validation, loss validation, confusion matrix, and ROC curve of all models are given in Figure 3.  
In Figure 4, the attributes affecting the 10-layer DNN model in the SHAP analysis of the positive class (copied) and their influence values are given. It is understood that especially the 'exam-type, A6, supervision, ques_solution_total_time' attributes affect the prediction variable more positively. Figure 5 shows the results of the LIME analysis. The model predicted the positive class 'cheated' correctly 72% of the time, and the most effective attributes in this prediction were 'exam_type, A6, A5'. The attributes 'A3, A8 and A12' were effective in predicting the negative class 'did not cheat'. When the values of the attributes are analyzed, the 'exam_type' attribute contributed to the 'copied' class prediction with a value of 0.92 and a rate of 8%.