A Multiindex Examination Cheating Detection Method Based on Neural Network 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 multisource plagiarism and low accuracy. In order to solve the disadvantages of traditional methods, this paper proposes a method for detecting cheating in multiindex examinations based on feedforward neural network. This paper first proposes RAE algorithm which combines linear regression and EM algorithm for studentsu0027 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 studentsu0027 cognitive level, seat distribution in the examination room, studentsu0027 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 feedforward 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.

Feature extraction and Classification of Learners using MultiContext Recurrent Neural Networks This study presents about a new procedure for extraction and classification of learners in a class using the neural networks. It is necessary to provide learning support corresponding to the understanding degree of each learner to improve learning process efficiency. For this purpose, this study develops a procedure to predict the achievement level of learners at the end of the class and classify them. A MultiContext Recurrent Neural Network (MCRNN) is used for predicting achievement level and classifying learners. By providing additional education for the learners who are classified as a low degree by the proposed method, it is expected to be able to take countermeasures for not becoming dropout in early stage. In this study, numerical experiments are executed to verify the usefulness of the proposed method. To gather enough number of learnersu0027 data, this study generates the learnersu0027 growth process data that used as training and test data of MCRNN. The experimental result indicates that the proposed method succeeded in classifying learners into three groups based on the understanding degree at the end of a class.