With the rapid growth of online education, Knowledge Tracing (KT) has become central to adaptive learning systems. Yet existing models struggle to integrate the multidimensional and heterogeneous signals generated during learning—such as exercise attributes, response behaviors, temporal factors, and hierarchical knowledge structure. Many methods rely on naive feature concatenation or fixed weighting, limiting their ability to capture synergistic interactions among features. We propose Gated full‐features Transformer Cognitive Knowledge Tracing (GCKT), a Transformer‐based model with a gated fusion mechanism that dynamically integrates multiple inputs. The model first embeds exercise, response correctness, response time, and hierarchical knowledge features (topics and concepts). Topic and concept embeddings are linearly projected into a unified knowledge representation. The exercise, time, correctness, and unified knowledge embeddings are then concatenated and passed through a learnable gating network (linear layer with sigmoid) to produce context‐aware importance weights. These weights are applied element‐wise to adaptively scale each feature before projection into a fused representation for the sequence encoder, enabling the Transformer to more accurately model the evolution of students’ cognitive states. Extensive experiments on public datasets, including MOOCRadar and Math, show that GCKT consistently outperforms strong baselines—such as DKT, AKT, and SAINT+—on key metrics (AUC and F1), delivering robust gains across settings. The results demonstrate that dynamic, fine‐grained feature fusion substantially improves KT performance and that GCKT offers a general, effective approach for modeling complex learning scenarios.

In the realm of online tutoring intelligent systems, e‐learners are exposed to a substantial volume of learning content. The extraction and organization of exercises and skills hold significant importance in establishing clear learning objectives and providing appropriate exercise recommendations. Presently, knowledge graph‐based recommendation algorithms have garnered considerable attention among researchers. However, these algorithms solely consider knowledge graphs with single relationships and do not effectively model exercise‐rich features, such as exercise representativeness and informativeness. Consequently, this paper proposes a framework, namely, the Knowledge Graph Importance‐Exercise Representativeness and Informativeness Framework, to address these two issues. The framework consists of four intricate components and a novel cognitive diagnosis model called the Neural Attentive Cognitive Diagnosis model to recommend the proper exercises. These components encompass the informativeness component, exercise representation component, knowledge importance component, and exercise representativeness component. The informativeness component evaluates the informational value of each exercise and identifies the candidate exercise set (EC) that exhibits the highest exercise informativeness. Moreover, the exercise representation component utilizes a graph neural network to process student records. The output of the graph neural network serves as the input for exercise‐level attention and skill‐level attention, ultimately generating exercise embeddings and skill embeddings. Furthermore, the skill embeddings are employed as input for the knowledge importance component. This component transforms a one‐dimensional knowledge graph into a multidimensional one through four class relations and calculates skill importance weights based on novelty and popularity. Subsequently, the exercise representativeness component incorporates exercise weight knowledge coverage to select exercises from the candidate exercise set for the tested exercise set. Lastly, the cognitive diagnosis model leverages exercise representation and skill importance weights to predict student performance on the test set and estimate their knowledge state. To evaluate the effectiveness of our selection strategy, extensive experiments were conducted on two types of publicly available educational datasets. The experimental results demonstrate that our framework can recommend appropriate exercises to students, leading to improved student performance.

Intelligent learning diagnosis is a critical engine of intelligent tutoring systems, which aims to estimate learners’ current knowledge mastery status and predict their future learning performance. The significant challenge with traditional learning diagnosis methods is the inability to balance diagnostic accuracy and interpretability. Although the existing psychometric‐based learning diagnosis methods provide some domain interpretation through cognitive parameters, they have insufficient modeling capability with a shallow structure for large‐scale learning data. While the deep learning‐based learning diagnosis methods have improved the accuracy of learning performance prediction, their inherent black‐box properties lead to a lack of interpretability, making their results untrustworthy for educational applications. To settle the abovementioned problem, the proposed unified interpretable intelligent learning diagnosis framework, which benefits from the powerful representation learning ability of deep learning and the interpretability of psychometrics, achieves a better performance of learning prediction and provides interpretability from three aspects: cognitive parameters, learner‐resource response network, and weights of self‐attention mechanism. Within the proposed framework, this paper presents a two‐channel learning diagnosis mechanism LDM‐ID as well as a three‐channel learning diagnosis mechanism LDM‐HMI. Experiments on two real‐world datasets and a simulation dataset show that our method has higher accuracy in predicting learners’ performances compared with the state‐of‐the‐art models and can provide valuable educational interpretability for applications such as precise learning resource recommendation and personalized learning tutoring in intelligent tutoring systems.