# Cheating Detection in Online Exams Using Deep Learning and Machine Learning
## 1 Introduction
In recent years, the adoption of learner-centered approaches by educational institutions, changes in access to information, taking into account the interests and needs of individuals, social disasters such as COVID19, and the increase in technological changes and transformations day by day have led to changes in the form of education, and as a result, distance education methods have been needed. Online exams allow learners to read questions on a technological screen, select and change answers with a mouse or keyboard, submit the exam at the end of the exam, and evaluate the exam results simultaneously [12]. In the continuation of this section of the study, studies on the prediction and classification of online exam data using DL and ML algorithms in the literature are included. In the literature, many deep learning and machine learning algorithms have been proposed to analyze online exam behaviors using online exam data, speech, images, mouse movements, etc. Good performances have been found from the proposed deep learning and machine learning models in studies that take into account mouse interaction such as clicking, scrolling, and keystrokes [13; 14; 15]. Word studies such as bag of words and stop words have been analyzed using Ngrams, stemming algorithms, and natural language processing with good results in applications [16; 17]. In studies aiming to detect cheating behavior by applying deep learning and machine learning models to students' online exam activities through camera and speech-based tools, good success has been achieved with the proposed models [18; 19; 20; 21]. [PERSON] et al. [22; 23] designed a system that will support teachers in monitoring and managing student behavior in online exams by using deep learning algorithms and computer vision algorithms. During the development phase of the system, feedback was provided through semi-structured interviews, but deep learning algorithms were applied to the data obtained from the images, and although high accuracies were obtained, difficulties were encountered in real-time exam data. Analyzing non-real-time but face-to-face exam papers, [PERSON] et al. [24] found that the XGBoost algorithm, one of the machine learning algorithms, performed with an accuracy rate of 94.8%. [PERSON] and [PERSON] [25] used reaction time, psychometric-based person fit statistics, and copy similarity measurement models to identify individuals who engaged in dishonest behavior in the test. K-nearest neighbor, naive Bayes, and random forest classification algorithms, which are among the supervised classification methods, were used. The Naive Bayes method achieved the best detection rate in identifying participants with a fraudulent behavior pattern with a sensitivity of 64%. Online application data in education has become the new focus of deep learning and machine learning methods. Today, with deep learning and machine learning algorithms, it is now possible to predict and classify student achievement, academic integrity, and support, revealing unethical behavior patterns from online applications. There are not many studies in this field in the literature. The main purpose of our study is to reveal good performance models with deep learning and machine learning algorithms in regression and classification problems using online exam data. This is the focal point of our research in order to make a contribution to the literature.