Knowledge tracing is a significant research area in educational data mining, aiming to predict future performance based on students’ historical learning data. In the field of programming, several challenges are faced in knowledge tracing, including inaccurate exercise representation and limited student information. These issues can lead to biased models and inaccurate predictions of students’ knowledge states. To effectively address these issues, we propose a novel programming knowledge tracing model named GPPKT (Knowledge Graph and Personalized Answer Sequences for Programming Knowledge Tracing), which enhances performance by using knowledge graphs and personalized answer sequences. Specifically, we establish the associations between well-defined knowledge concepts and exercises, incorporating student learning abilities and latent representations generated from personalized answer sequences using Variational Autoencoders (VAE) in the model. This deep knowledge tracing model employs Long Short-Term Memory (LSTM) networks and attention mechanisms to integrate the embedding vectors, such as exercises and student information. Extensive experiments are conducted on two real-world programming datasets. The results indicate that GPPKT outperforms state-of-the-art methods, achieving an AUC of 0.8840 and an accuracy of 0.8472 on the Luogu dataset, and an AUC of 0.7770 and an accuracy of 0.8799 on the Codeforces dataset. This demonstrates the superiority of the proposed model, with an average improvement of 9.03% in AUC and 2.02% in accuracy across both datasets.

Despite the large-scale adoption of Artificial Intelligence (AI) models in healthcare, there is an urgent need for trustworthy tools to rigorously backtrack the model decisions so that they behave reliably. Counterfactual explanations take a counter-intuitive approach to allow users to explore “what if" scenarios gradually becoming popular in the trustworthy field. However, most previous work on model’s counterfactual explanation cannot generate in-distribution attribution credibly, produces adversarial examples, or fails to give a confidence interval for the explanation. Hence, in this paper, we propose a novel approach that generates counterfactuals in locally smooth directed semantic embedding space, and at the same time gives an uncertainty estimate in the counterfactual generation process. Specifically, we identify low-dimensional directed semantic embedding space based on Principal Component Analysis (PCA) applied in differential generative model. Then, we propose latent space smoothing regularization to rectify counterfactual search within in-distribution, such that visually-imperceptible changes are more robust to adversarial perturbations. Moreover, we put forth an uncertainty estimation framework for evaluating counterfactual uncertainty. Extensive experiments on several challenging realistic Chest X-ray and CelebA datasets show that our approach performs consistently well and better than the existing several state-of-the-art baseline approaches.