Student performance prediction is a fundamental task in online learning systems, which aims to provide students with access to active learning. Generally, student performance prediction is achieved by tracing the evolution of each student's knowledge states via a series of learning activities. Every learning activity record has two types of feature data: student behavior and exercise features. However, most methods use features that are related to exercises, such as correctness and concepts, while other student behavior features are usually ignored. The few studies that have focused on student behavior features through subjective manual selection argue that different student behavior features can be used in an equivalent manner to predict student performance. In this paper, we assume that the integration of student behavior features and exercise features is crucial to improve the precision of prediction, and each feature has a different impact on student performance. Therefore, this paper proposes a novel framework for student performance prediction by making full use of both student behavior features and exercise features and combining the attention mechanism with the knowledge tracing model. Specifically, we first exploit machine learning to capture feature representation automatically. Then, a fusion attention mechanism based on recurrent neural network architecture is used for student performance prediction. Extensive experiments on a real-world dataset show the effectiveness and practicability of our approach. The accuracy of our method is up to 98%, which is superior to previous methods.
The Two-dimensional (2-D) maximum Tsallis entropy method takes advantage of the spatial neighbor information with using the 2-D histogram of the image and has a controllable parameter, so it often gets ideal segmentation results even when the image signal noise ratio (SNR) is low. However, its timeconsuming computation is often an obstacle in real time application systems. In this paper, a fast image segmentation algorithm based on recurring and chaos optimization algorithm (COA) for 2-D Tsallis entropy is presented. Firstly, the traditional COA is improved, and then the improved COA, which can get global solution with lower computational load in the process of solving the 2-D maximum Tsallis entropy problem, is combined with recurring with the stored matrix variables to greatly reduce computational cost. Experimental results show the proposed approach can get better segmentation results with less computation cost.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.