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.

Abstract In an online learning system, the automatic scoring of an essay is key to providing immediate feedback on essays submitted by students. To the best of our knowledge, existing approaches ignore the multidimensional and heterogeneous characteristics of essays or rely too heavily on the manual creation of features; therefore, a more comprehensive method of scoring essays is required. To address this issue, this paper proposes an enhanced hybrid neural network for automated essay scoring that extracts and fuses the linguistic, semantic, and structural attributes of an essay to achieve a comprehensive representation. Specifically, linguistic attributes include not only lexical features extracted from the words of an essay but also syntactic features obtained from sentences and syntax trees. Semantic attributes include the dynamic textual semantic representation and topic similarity obtained by the text encoder. We also considered the structural attributes. The text encoder provides the overall structural representation, while the sentence similarity matrix provides the two spatial features of connectivity and aggregation. Finally, we fused the three attributes and six features to achieve a more objective and comprehensive automatic scoring. We found that our model improves the Kappa index by an average of 1.4% over the current best model when tested against four state‐of‐the‐art models using eight public data sets.