Deep learning ( DL ) based knowledge tracing ( KT ) models have challenges for uninterpretable prediction and parameter representation in educational applications, though they achieved remarkable outcomes in predicting the exercise performance of students. This paper proposes a novel knowledge tracing model of high precision and interpretability (named XKT ) for questions with multiple knowledge concepts based on cognitive learning theories and multidimensional item response theory ( MIRT ). The XKT consists of three differentiable network components: multi-feature embedding, cognition processing network, and MIRT -based neural predictor, which aim to provide an explainable prediction of student exercise performance. Specifically, in XKT , multi-feature embedding learns the rich semantic representation (e.g., knowledge distribution information) to enhance knowledge tracing using a cognition processing network. The cognition processing network performs selective perception, ability memory processing, and long-term knowledge memory processing to ensure the explainable factor representation for the MIRT -based neural predictor. Lastly, the MIRT -based neural predictor employs psychometric parameters to interpret student exercise predictions better. Extensive experiments on four real-world datasets show that XKT outperforms existing KT methods in predicting future learner responses. Moreover, ablation studies further show that XKT offers good interpretability of student performance predictions with multiple knowledge concepts, indicating excellent potential in real-world educational applications.

Three-dimensional point cloud classification is fundamental but still challenging in 3-D vision. Existing graph-based deep learning methods fail to learn both low-level extrinsic and high-level intrinsic features together. These two levels of features are critical to improving classification accuracy. To this end, we propose a dual-graph attention convolution network (DGACN). The idea of DGACN is to use two types of graph attention convolution operations with a feedback graph feature fusion mechanism. Specifically, we exploit graph geometric attention convolution to capture low-level extrinsic features in 3-D space. Furthermore, we apply graph embedding attention convolution to learn multiscale low-level extrinsic and high-level intrinsic fused graph features together. Moreover, the points belonging to different parts in real-world 3-D point cloud objects are distinguished, which results in more robust performance for 3-D point cloud classification tasks than other competitive methods, in practice. Our extensive experimental results show that the proposed network achieves state-of-the-art performance on both the synthetic ModelNet40 and real-world ScanObjectNN datasets.

In the power industry, processing business big data from geographically distributed locations, such as online line-loss analysis, has emerged as an important application. How to achieve highly efficient big data storage to meet the requirements of low latency processing applications is quite challenging. In this paper, we propose a novel adaptive power storage replica management system, named PARMS, based on stochastic configuration networks (SCNs), in which the network traffic and the data center (DC) geo-distribution are taken into consideration to improve data real-time processing. First, as a fast learning model with less computation burden and sound prediction performance, the SCN model is employed to estimate the traffic state of power data networks. Then, a series of data replica management algorithms is proposed to lower the effects of limited bandwidths and a fixed underlying infrastructure. Last, the proposed PARMS is implemented using data-parallel computing frameworks (DCFs) for the power industry. Experiments are carried out in an electric power corporation of 230 million users, CSG, and the results show that our proposed solution can deal with power big data storage efficiently and the job completion times across geo-distributed DCs are reduced by 12.19% on average.

Academic performance is a crucial issue in the field of Online learning analytics. While deep learning-based models have made significant progress in the era of big data, many of these methods need help to capture the complex relationships present in online learning activities and student attributes, which are essential for improving prediction accuracy. We present a novel model for predicting academic performance in this paper. This model harnesses the power of dual graph neural networks to effectively utilize both the structural information derived from interaction activities and the attribute feature spaces of students. The proposed model uses an interaction-based graph neural network module to learn local academic performance representations from online interaction activities and an attribute-based graph neural network to learn global academic performance representations from attribute features of all students using dynamic graph convolution operations. The learned representations from local and global levels are combined in a local-to-global representation learning module to generate predicted academic performances. The empirical study results demonstrate that the proposed model significantly outperforms existing methods. Notably, the proposed model achieves an accuracy of 83.96% for predicting students who pass or fail and an accuracy of 90.18% for predicting students who pass or withdraw on a widely recognized public dataset. The ablation studies confirm the effectiveness and superiority of the proposed techniques.