Academic integrity in higher education can be influenced by individual or by institutional factors. Cheating behavior undermines the academic integrity of the learning environment and can have negative consequences for both the individual student and the academic community. To understand the factors that influence the cheating behavior of students, a quantitative study was conducted, specifically focusing on the types of exams and assignments that are most susceptible to cheating. The collected data has been analysed with Machine Learning methods and the results have been visualised. This survey is a part of a dissertation project and the survey results will be used for an eye-tracking experiment to measure cheating behavior of students. Long-term aim is to develop online exam methods which are not susceptible to certain cheating methods.