Student cognitive modeling is a fundamental task in the intelligence education field. It serves as the basis for various downstream applications, such as student profiling, personalized educational content recommendation, and adaptive testing. Cognitive Diagnosis (CD) and Knowledge Tracing (KT) are two mainstream categories for student cognitive modeling, which measure the cognitive ability from a limited time (e.g., an exam) and the learning ability dynamics over a long period (e.g., learning records from a year), respectively. Recent efforts have been dedicated to the development of open-source code libraries for student cognitive modeling. However, existing libraries often focus on a particular category and overlook the relationships between them. Additionally, these libraries lack sufficient modularization, which hinders reusability. To address these limitations, we have developed a unified PyTorch-based library EduStudio, which unifies CD and KT for student cognitive modeling. The design philosophy of EduStudio is from two folds. From a horizontal perspective, EduStudio employs the modularization that separates the main step pipeline of each algorithm. From a vertical perspective, we use templates with the inheritance style to implement each module. We also provide eco-services of EduStudio, such as the repository that collects resources about student cognitive modeling and the leaderboard that demonstrates comparison among models. Our open-source project is available at the website of edustudio.ai.
Multivariate time series (MTS) data are vital for various applications, particularly in machine learning tasks. However, challenges such as sensor failures can result in irregular and misaligned data with missing values, thereby complicating their analysis. While recent advancements use graph neural networks (GNNs) to manage these Irregular Multivariate Time Series (IMTS) data, they generally require a reliable graph structure, either pre-existing or inferred from adequate data to properly capture node correlations. This poses a challenge in applications where IMTS data are often streamed and waiting for future data to estimate a suitable graph structure becomes impractical. To overcome this, we introduce a dynamic GNN model suited for streaming characteristics of IMTS data, incorporating an instance-attention mechanism that dynamically learns and updates graph edge weights for real-time analysis. We also tailor strategies for high-frequency and low-frequency data to enhance prediction accuracy. Empirical results on real-world datasets demonstrate the superiority of our proposed model in both classification and imputation tasks.
The remarkable success in graph neural networks (GNNs) promotes the explainable graph learning methods. Among them, the graph rationalization methods draw significant attentions, which aim to provide explanations to support the prediction results by identifying a small subset of the original graph (i.e., rationale). Although existing methods have achieved promising results, recent studies have proved that these methods still suffer from exploiting shortcuts in the data to yield task results and compose rationales. Different from previous methods plagued by shortcuts, in this paper, we propose a Shortcut-guided Graph Rationalization (SGR) method, which identifies rationales by learning from shortcuts. Specifically, SGR consists of two training stages. In the first stage, we train a shortcut guider with an early stop strategy to obtain shortcut information. During the second stage, SGR separates the graph into the rationale and non-rationale subgraphs. Then SGR lets them learn from the shortcut information generated by the frozen shortcut guider to identify which information belongs to shortcuts and which does not. Finally, we employ the non-rationale subgraphs as environments and identify the invariant rationales which filter out the shortcuts under environment shifts. Extensive experiments conducted on synthetic and real-world datasets provide clear validation of the effectiveness of the proposed SGR method, underscoring its ability to provide faithful explanations.
In this paper, we present SHARPSMT, a toolkit for measuring solution spaces of SMT(LA) formulas which are Boolean combinations of linear arithmetic constraints, i.e., #SMT(LA) problems. It integrates SMT satisfiability solving algorithm with various polytope subroutines: volume computation, volume estimation, lattice counting, and approximate lattice counting. We propose a series of new polytope preprocessing techniques which have been implemented in SHARPSMT. Experimental results show that the new polytope preprocessing techniques are very effective, especially on application instances. We believe that SHARPSMT will be useful in a number of areas.
