Programming knowledge tracing (programming KT) aims to analyze the dynamic programming states in solving problems based on historical behaviors and predict future performance. In programming, a student’s thought process can lead to multiple solutions for the same problem. However, current programming KT models attribute learners’ responses only to knowledge mastery and ability, overlooking thought factors, which creates a contradiction between data fitting and the rationalization of the model inference process. To address this, we propose a problem solving-oriented programming KT (SPKT) method that incorporates programming knowledge, computational thinking, and solving ability to improve attribution accuracy. Specifically, we designed a dual-channel attention network based on the principle of initiative gain to retrieve knowledge. Additionally, we utilized the edit tree distance algorithm to capture fine-grained trajectory representations and employed a redress mechanism with gating to update abilities based on code information. Experiments demonstrate SPKT’s superiority.

Knowledge Tracing (KT) is a critical service in distance education, predicting students' future performance based on their responses to learning resources. The reasonable assessment of the knowledge state, along with accurate response prediction, is crucial for KT. However, existing KT methods prioritize fitting results and overlook attention to the problem-solving process. They equate the knowledge students memorize before problem-solving with the knowledge that can be acquired or applied during problem-solving, leading to dramatic fluctuations in knowledge states between mastery and non-mastery, with low interpretability. This paper explores knowledge transformation in problem-solving and proposes an interpretable model, Problem-solving Knowledge Tracing (PSKT). Specifically, we first present a knowledge-centered problem representation that enhances its expression by adjusting problem variability. Then, we meticulously designed a Sequential Neural Network (SNN) with three stages: (1) Before problem-solving, we model students' personalized problem space and simulate their acquisition of problem-related knowledge through a gating mechanism. (2) During problem-solving, we evaluate knowledge application and calculate response with a four-parameter IRT. (3) After problem-solving, we quantify student knowledge internalization and forgetting using an incremental indicator. The SNN, inspired by problem-solving and constructivist learning theories, is an interpretable model that attributes learner performance to subjective problems (difficulty, discrimination), objective knowledge (knowledge acquisition and application), and behavior (guessing and slipping). Experimental results show PSKT's advantages in prediction accuracy, reasonable knowledge state assessment, and learning process explanation. The code is available at https://github.com/Oia-10/PSKT.

With the proliferation of fake news, the spread of misleading information can easily cause social panic and group polarization. Many existing methods for detecting fake news rely on linguistic and semantic features extracted from the content of the news. Some existing approaches focus on sentiment analysis for fake news detection, but the sentiment changes and sentence-level emotional interactions in news classification are not fully analyzed. Fortunately, we observe that in long-form news, the change and mutual influence of sentiment between sentences are different. To extract the features of sentiment interaction between sentences in the article, we propose a graph attention network-based model that combines both sentiment and external knowledge comparison to meet the needs of fake news classification. We obtain the contextual sentiment representation and entity representation of the sentence through the heterogeneous network and the emotion interaction network and obtain the change of the sentiment vector through the emotion comparison network. We compare the entity vectors in the context with those corresponding knowledge base (KB)-based, combine them with the contextual semantic representation of the sentence, and finally input them into the classifier. In experiments, our model performs well in both single and multiclass classification, achieving the state-of-the-art accuracy on existing datasets.