Knowledge tracing (KT) aims to predict students' responses to practices based on their historical question-answering behaviors. However, most current KT methods focus on improving overall AUC, leaving ample room for optimization in modeling sequences of excessive or insufficient lengths. As sequences get longer, computational costs will increase exponentially. Therefore, KT methods usually truncate sequences to an acceptable length, which makes it difficult for models on online service systems to capture complete historical practice behaviors of students with too long sequences. Conversely, modeling students with short practice sequences using most KT methods may result in overfitting due to limited observation samples. To address the above limitations, we propose a model called Sequence-Flexible Knowledge Tracing (SFKT). Specifically, to flexibly handle long sequences, SFKT introduces a total-term encoder to effectively model complete historical practice behaviors of students at an affordable computational cost. Additionally, to improve the prediction accuracy of students with short practice sequences, we introduce a contrastive learning task and data augmentation schema to improve the generality of modeling short sequences by constructing more learning objectives. Extensive experimental results show that SFKT achieves significant improvements over multiple benchmarks, demonstrating the value of exploring the modeling of sequences of excessive or insufficient lengths. Our code is available at https://github.com/zmy-9/SFKT.

As the core of the Knowledge Tracking (KT) task, assessing students' dynamic mastery of knowledge concepts is crucial for both offline teaching and online educational applications. Since students' mastery of knowledge concepts is often unlabeled, existing KT methods focus on predicting students' responses to practices. However, purely predicting student responses without imposing specific constraints on hidden concept mastery values does not guarantee the accuracy of these intermediate values as concept mastery values. To address this issue, we propose a principled approach called Counterfactual Monotonic Knowledge Tracing (CMKT), which builds on the implicit paradigm described above by using a counterfactual assumption to constrain the evolution of students' mastery of knowledge concepts. Specifically, CMKT first assesses students' knowledge concept mastery value based on their historical practice sequences. Then, CMKT sets the answer of the most recent practice as the opposite of the actual answer and, based on this counterfactual answer, assesses the student's corresponding counterfactual knowledge mastery value. During the model training process, CMKT constrains the update of the student's knowledge states by ensuring that the two types of knowledge mastery values of students satisfy a fundamental educational theory, the monotonicity theory, to provide specific semantics for the assessed mastery values by the model. Finally, extensive experiments on five datasets demonstrate the superiority of CMKT over baseline models.

The Knowledge Tracing (KT) task plays a crucial role in personalized learning, and its purpose is to predict student responses based on their historical practice behavior sequence. However, the KT task suffers from data sparsity, which makes it challenging to learn robust representations for students with few practice records and increases the risk of model overfitting. Therefore, in this paper, we propose a Cognition-Mode Aware Variational Representation Learning Framework (CMVF) that can be directly applied to existing KT methods. Our framework uses a probabilistic model to generate a distribution for each student, accounting for uncertainty in those with limited practice records, and estimate the student’s distribution via variational inference (VI). In addition, we also introduce a cognition-mode aware multinomial distribution as prior knowledge that constrains the posterior student distributions learning, so as to ensure that students with similar cognition modes have similar distributions, avoiding overwhelming personalization for students with few practice records. At last, extensive experimental results confirm that CMVF can effectively aid existing KT methods in learning more robust student representations. Our code is available at https://github.com/zmy-9/CMVF.

Multivariate time series anomaly detection is a challenging task due to the intricate intra- and inter-metric dependencies present in the data. Previous methods have mainly focused on capturing intra-metric dependency, while neglecting the utilization of the dependency information across metrics. More recent approaches have considered inter-metric dependency, but have not been able to capture the complex inter-metric dynamics accurately and adaptively. To address these challenges, we propose a novel dual-attentional multivari-ate time series anomaly detection framework. Our approach improves on the original Dot-Product Attention to robustly capture intra-metric dependency. Additionally, we introduce automatic graph structure learning and graph attention mechanism to adaptively capture inter-metric dependency and utilize it effectively. Experiments on five public datasets from different domains demonstrate that comprehensive and accurate modeling of both dependencies enables our proposed method to outperform baseline methods in accurately detecting anomalies.

With the increasing demands of personalized learning, knowledge tracing has become important which traces students' knowledge states based on their historical practices. Factor analysis methods mainly use two kinds of factors which are separately related to students and questions to model students' knowledge states. These methods use the total number of attempts of students to model students' learning progress and hardly highlight the impact of the most recent relevant practices. Besides, current factor analysis methods ignore rich information contained in questions. In this paper, we propose Multi-Factors Aware Dual-Attentional model (MF-DAKT) which enriches question representations and utilizes multiple factors to model students' learning progress based on a dual-attentional mechanism. More specifically, we propose a novel student-related factor which records the most recent attempts on relevant concepts of students to highlight the impact of recent exercises. To enrich questions representations, we use a pre-training method to incorporate two kinds of question information including questions' relation and difficulty level. We also add a regularization term about questions' difficulty level to restrict pre-trained question representations to fine-tuning during the process of predicting students' performance. Moreover, we apply a dual-attentional mechanism to differentiate contributions of factors and factor interactions to final prediction in different practice records. At last, we conduct experiments on several real-world datasets and results show that MF-DAKT can outperform existing knowledge tracing methods. We also conduct several studies to validate the effects of each component of MF-DAKT.