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As students’ behaviors are important factors that can reflect their learning styles and living habits on campus, extracting useful features of them plays a helpful role in understanding the students’ learning process, which is an important step towards personalized education. Recently, the task of predicting students’ performance from their campus behaviors has aroused the researchers’ attention. However, existing studies mainly focus on extracting statistical features manually from the pre-stored data, resulting in hysteresis in predicting students’ achievement and finding out their problems. Furthermore, due to the limited representation capability of these manually extracted features, they can only understand the students’ behaviors shallowly. To make the prediction process timely and automatically, we treat the performance prediction task as a short-term sequence prediction problem, and propose a two-stage classification framework, i.e., Sequence-based Performance Classifier (SPC), which consists of a sequence encoder and a classic data mining classifier. More specifically, to deeply discover the sequential features from students’ campus behaviors, we first introduce an attention-based Hybrid Recurrent Neural Network (HRNN) to encode their recent behaviors by giving a higher weight to the ones that are related to the students’ last action. Then, to conduct student performance prediction, we further involve these learned features to the classic Support Vector Machine (SVM) algorithm and finally achieve our SPC model. We conduct extensive experiments in the real-world student card dataset. The experimental results demonstrate the superiority of our proposed method in terms of Accuracy and Recall.

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Influential nodes are rare in social networks, but their influence can quickly spread to most nodes in the network. Identifying influential nodes allows us to better control epidemic outbreaks, accelerate information propagation, conduct successful e-commerce advertisements, and so on. Classic methods for ranking influential nodes have limitations because they ignore the impact of the topology of neighbor nodes on a node. To solve this problem, we propose a novel measure based on local centrality with a coefficient. The proposed algorithm considers both the topological connections among neighbors and the number of neighbor nodes. First, we compute the number of neighbor nodes to identify nodes in cluster centers and those that exhibit the "bridge" property. Then, we construct a decreasing function for the local clustering coefficient of nodes, called the coefficient of local centrality, which ranks nodes that have the same number of four-layer neighbors. We perform experiments to measure node influence on both real and computer-generated networks using six measures: Degree Centrality, Betweenness Centrality, Closeness Centrality, K-Shell, Semi-local Centrality and our measure. The results show that the rankings obtained by the proposed measure are most similar to those of the benchmark Susceptible-Infected-Recovered model, thus verifying that our measure more accurately reflects the influence of nodes than do the other measures. Further, among the six tested measures, our method distinguishes node influence most effectively.

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