# Cheating Detection in Online Exams Using Deep Learning and Machine Learning
## 4 Discussion  
In this study, deep learning and machine learning-based algorithms were used to perform regression and classification of unethical behaviors in online distance education exams. The main objective of the article is to compare the performances of deep learning and machine learning algorithms using appropriate parameters and to observe the effects of attributes on performance through descriptive analysis. As a result of the literature review, similar methods and studies on the data used are presented in Table A1 in the Appendix A. Table A1 compares the performance values of the proposed models since they have been studied with similar data sets in the literature. An article by [PERSON] and [PERSON] included data from 1636 exam participants, 10 demographic questions, 170 double-point item responses, and 170 Likert-type items. In the analyses performed, the same accuracy and specificity rates were obtained in all models. Naive Bayes has the highest sensitivity value with a rate of 64% [14]. In their study, [PERSON] et al. aimed to detect online cheating behavior with 118 student data by proposing K-nearest neighbor, naive Bayes, and gradient boosting algorithms. For the classification of copying behavior, ChatGPT-generated responses and rephrased ChatGPT responses were detected. In the binary classification using the stop word list and stemming algorithm, Random Forest showed the highest performance with a rate of 87% [17]. In a paper by [PERSON] using the open access 7 WiseUp dataset, the performance of the LSTM model was observed to be 90% [21].  
There are few studies in the literature that propose models using deep learning and machine learning algorithms on real online exam data. The most important features that distinguish our study from many other studies are the use of both real exam data and structured exam data prepared by the researchers and the use of exam question answering times. In addition, the model we propose is more advantageous than the costly use of biometric devices that record student behavior and limit privacy [49; 50; 51], as seen in many studies. In the regression analysis we conducted within the scope of Scenario-1, the best parameters of the 5-layer DNN model we proposed showed a good performance with a test performance value of R\({}^{2}\) = 0.809. This behavior of the model explains well the variance  
Figure 8: LIME analysis for the XGBoost Model.  
of the independent variables on the dependent variable [52]. As the number of layers in the layered structure increased, the performance of the model increased linearly up to a certain layer, but too many layers started to decrease performance due to over-learning. In our study, in order to achieve the best performance, the dropout layer prevented the inclusion of randomly selected neurons in the training data. In Table A1, the LSTM model proposed by [PERSON] was used for large data sets, although its success is high. The data set in our study is not effective in training the LSTM model because it is small [21; 53; 54]. In our analysis with binary classification in Scenario-2, the 10-layer DNN model showed good performance with an accuracy value of 96.9%, and when other metrics are taken into account, it gives promising results compared to the models [55]. In the triple classification analysis within the scope of Scenario-3, the XGBoost model that we recommended among the machine learning algorithms appears as the strongest model with 97.7% accuracy, 95.8% sensitivity, 98.5% specificity, and AUC = 1 performance values.  
The main limitations of our study are the small number of online exam participants, the lack of always-available online access, and technical problems with access.  
If the number of online exam participants is increased in the future, other algorithms of deep learning can be improved. Different exam scenarios can be created, and analyses can be made to detect cheating. By further developing the current study, the student tracking system can be made available to teachers.