A Multi-index Examination Cheating Detection Method Based on Neural Network

摘要

Students cheating in exams destroys the fair principle of evaluation and affects the normal teaching order of the school. Therefore, the examination cheating detection has the vital significance. The existing cheating detection methods have disadvantages such as insufficient modeling accuracy for students, lag in cognitive diagnosis, difficulty in detecting multi-source plagiarism and low accuracy. In order to solve the disadvantages of traditional methods, this paper proposes a method for detecting cheating in multi-index examinations based on feed-forward neural network. This paper first proposes RAE algorithm which combines linear regression and EM algorithm for students' cognitive diagnosis. We use RAE algorithm and LSTM neural network to obtain the knowledge point mastery degree of each student based on the history problem solving and the knowledge point mastery degree based on the exam problem solving. Then, according to the information of students' cognitive level, seat distribution in the examination room, students' habit of guessing answers at normal times, similarity of examination papers, etc., we get several indicators to judge whether students cheat. Finally, we take various indicators obtained through various methods as characteristics and use feed-forward neural network to classify whether students cheat or not. The experimental results show that the accuracy and recall of this method are significantly higher than those of several popular methods.