Knowledge Tracing (KT) is the task of modeling a learner’s knowledge state to predict future performance in e-learning systems based on past performance. Deep learning-based methods, such as recurrent neural networks, memory-augmented neural networks, and attention-based neural networks, have recently been used in KT. Such methods have demonstrated excellent performance in capturing the latent dependencies of a learner’s knowledge state on recent exercises. However, these methods have limitations when it comes to dealing with the so-called Skill Switching Phenomenon (SSP), i.e., when learners respond to exercises in an e-learning system, the latent skills in the exercises typically switch irregularly. SSP will deteriorate the performance of deep learning-based approaches for simulating the learner’s knowledge state during skill switching, particularly when the association between the switching skills and the previously learned skills is weak. To address this problem, we propose the Memory-augmented Attentive Network (MAN), which combines the advantages of memory-augmented neural networks and attention-based neural networks. Specifically, in MAN, memory-augmented neural networks are used to model learners’ longer term memory knowledge, while attention-based neural networks are used to model learners’ recent term knowledge. In addition, we design a context-aware attention mechanism that automatically weighs the tradeoff between these two types of knowledge. With extensive experiments on several e-learning datasets, we show that MAN effectively improve predictive accuracies of existing state-of-the-art DLKT methods.

Multi-Document Scientific Summarization (MDSS) aims to produce coherent and concise summaries for clusters of topic-relevant scientific papers. This task requires precise understanding of paper content and accurate modeling of cross-paper relationships. Knowledge graphs convey compact and interpretable structured information for documents, which makes them ideal for content modeling and relationship modeling. In this paper, we present KGSum, an MDSS model centred on knowledge graphs during both the encoding and decoding process. Specifically, in the encoding process, two graph-based modules are proposed to incorporate knowledge graph information into paper encoding, while in the decoding process, we propose a two-stage decoder by first generating knowledge graph information of summary in the form of descriptive sentences, followed by generating the final summary. Empirical results show that the proposed architecture brings substantial improvements over baselines on the Multi-Xscience dataset.

In computer-aided education systems, Deep Learning-based Knowledge Tracing (DLKT) models outperform traditional models on tracing learners’ knowledge in recent years. First, we propose a new Performance Factors-based Embedding (PFE) model for DLKT by extending learner’s historical performances on exercises into the existing Rasch Model-based Embedding (RME) model. Second, we find that side factors (e.g., template and hint) are helpful to reflect the individualized difficulties of different exercises covering the same skill by analysing data. Therefore, we introduce an extensible embedding framework to synthesize skill, exercise, performance factors and side factors, dubbed BPS. BPS consists of three components: base embedding, performance embedding and side embedding which allows one or more side factors related to the difficulty of exercise to be extended in BPS. Finally, experiments on three real-world benchmark datasets show that PFE and BPS significantly improve the state-of-the-art DLKT model on predicting future learner responses.

This paper analyzes the limitation of baseline estimation by defining four kinds of rating personalization corresponding to four kinds of users' rating criterions, including Normal, Strict, Lenient, and Middle. We find a standard deviation proportion pattern from ratings' normal distribution to enhance the handling capability of users' personalized rating behavior, and propose a novel baseline estimation model based on Standard Deviation Proportion, named SDP model, to improve the accuracy of existing recommendation algorithms which used traditional baseline estimation. We also propose two application instances of SDP, including SDPSVD++ and SDPTrustSVD, to show how to apply the proposed SDP. Experiments show that the SDP can not only improve the baseline estimation performance, but also can effectively improve predictive accuracies of existing recommendation algorithms.