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Student performance prediction is a critical task in supporting decision-making for Intelligent Tutoring Systems (ITS). Correct predictions of student performance are prerequisites for ITS to supply intelligent services and optimize learning efficiency, e.g., recommending the most appropriate learning resources for each student. Existing methods mainly include cognitive diagnosis and knowledge tracing, both of which focus on students' cognitive modeling based on their interactions on a sequence of items and give predictions by assessing if their cognitive states can meet the item requirements. Specifically, cognitive diagnosis only considers students' global static cognitive states, while knowledge tracing focuses on students' local dynamics. However, both global and local features are critical for predicting student performance. Therefore, in this paper, we propose a novel G lobal and L ocal N eural C ognitive (GLNC) model to capture both global and local features in student-item interactions for more accurate predictions. Specifically, we first learn students' global cognitive level according to all student-item interactions. Then, we propose a self-attentive encoder based on the scaled dot-product attention mechanism to extract the local cognitive dynamics and the dependencies between students' recent interactions. Finally, to obtain better predictions, we design a fused gate based on the similarity between students' recently responded items and the item to be predicted to adaptively combine the global and local features. To evaluate the effectiveness of GLNC, we compare it with both cognitive diagnosis and knowledge tracing methods. All experiments are conducted on three public datasets that contain real student-item interactions on mathematics or language courses from various ITS. The experimental results demonstrate that GLNC achieves an average score of 0.7810 on the AUC metric, 0.7627 on the ACC metric, 0.3987 on the RMSE metric, 0.2023 on the r 2 metric, respectively achieving an average improvement of 1.84%, 1.07%, 1.87%, and 11.38% in contrast to existing state-of-the-art methods (i.e., NCD and LPKT). Moreover, we further analyze the performance of GLNC under different probabilities of guessing and slipping, the results indicate that GLNC is more robust against the influence of noisy data while considering both global and local features. Benefiting from the superior accuracy and stability, our proposed GLNC has a wide range of potential implications for ITS, which can be easily applied to improve students' learning efficiency and experience. • Cognitive diagnosis focuses on students' global static cognitive states. • Knowledge tracing measures students' local dynamics in learning. • It is critical to make trade-offs between global and local features. • Our method achieves more precise and robust results on performance prediction. [ABSTRACT FROM AUTHOR]

In modern education, personalized learning has garnered increasing attention, with cognitive diagnosis (CD) serving as a fundamental component. The objective of CD is to deeply comprehend students' cognitive abilities, thereby identifying individual differences and providing support for personalized learning. However, many existing models ignore the influence of data noise and confidence in students' responses. In addition, most existing methods equally extract evidence of students' strengths and weaknesses from their overall responses, disregarding the characteristics of various types of relationships in students' correct and incorrect responses. To this end, this study proposes a Confidence-guided Multigraph Model (CMM) for CD. First, to capture the changes and uncertainties in the student's cognitive process, CMM dynamically calculates the confidence scores of the students' responses. Second, CMM constructs multiple graphs to represent various relationships in students' correct and incorrect responses. Then, CMM aggregates the unique information within the students' correct and incorrect responses from various graphs. Finally, CMM uses a joint training strategy of multigraph iteration to simulate students' cognitive ability in multiple dimensions. Extensive experimental results on two real-world datasets clearly demonstrate the efficacy of our proposed model. This innovative approach provides a novel perspective for the development of CD and lays the foundation for more accurate personalized learning support. • We considered confidence information to reduce the impact of data noise and sparsity. • A model using multi-graph to represent different types of relationships is developed. • We design a joint training strategy to integrate multiple levels of information. • Our model achieves more precise and robust results in diagnosis results. [ABSTRACT FROM AUTHOR]

Knowledge Tracing (KT) focuses on modeling the exercise process of students, assessing their knowledge state changes during the exercise process, and further providing targeted guidance for teaching and learning. Current KT models have made significant progress by utilizing deep learning technologies to model relevant attributes of the questions. However, in current knowledge tracing strategies, we have yet to exhaustively explore how to precisely evaluate the hierarchies of students' knowledge mastery to better discern their current target hierarchy. To address the limitations of the KT model in accurately identifying and positioning target hierarchy, our research focuses on two key areas: first, we dynamically track students' target hierarchies by evaluating their feedback on questions at different hierarchies ; second, we assess their actual question-solving abilities within these target hierarchies, thereby optimizing our evaluation of their knowledge status. Based on these insights, we propose the T arget H ierarchy-guided K nowledge T racing ( THKT ) model. In this model, we first incorporate the identification of question hierarchy into the model representation. Then, to avoid homogenization of the target hierarchy, we dynamically track the appropriate hierarchy for students based on their varying feedback to questions of the same hierarchy, thereby pinpointing their learning target hierarchies. Simultaneously, we develop a KCs applied ability learning module that works in conjunction with the target hierarchies to generate interpretable prediction results. The proposed THKT model has been tested and evaluated using three public, real-world educational datasets. The findings clearly demonstrate that our approach shines in the realm of KT prediction tasks, providing significantly interpretability. For broader research, we plan to provide the source code at: https://github.com/xinjiesun-ustc/THKT to ensure accessibility and promote further innovation in this field. • First-time introduction of personalized target hierarchy in knowledge tracing. • THKT smartly integrates students' target hierarchies and problem-solving skills. • THKT-guided tracing offers superior adaptability and hierarchical interpretability. • Experiments validate the THKT model and target hierarchy effectiveness. • Source code is open at https://github.com/xinjiesun-ustc/THKT. [ABSTRACT FROM AUTHOR]

In Self-Directed Learning Systems (SDLS), suggesting specific exercises to students after viewing educational videos is crucial, which promotes the integration of learning resources and improves learning efficiency. Given that exercises and educational videos are distinct learning resources, suggesting exercises centered on educational videos containing knowledge concepts is referred to as cross-type recommendations. Traditional cross-type recommendation methods usually separate data representation and recommendation tasks, which hampers effective knowledge transfer and information fusion. Furthermore, educational resources frequently feature an irregular distribution of information, posing a significant challenge to existing models in extracting crucial information. Besides, due to the inadequate data, the cross-type recommendation system needs help in developing a robust model. To address these challenges, we propose a Multi-task Information Enhancement Recommendation (MIER) Model in this paper. This model provides a comprehensive recommendation capability by integrating resource representation and merging recommendations. Multi-task learning improves the capacity to integrate information and transfer knowledge. Then, we propose an Information Enhancement Encoder (IEE) module that utilizes the attention mechanism to enhance the model's ability to extract essential information from educational videos and exercises. Finally, to address the problem of inadequate data, we design a fusion structure that incorporates the concept knowledge graph as expert information into the model. To evaluate the effectiveness of the MIER, we compare it with other models on both predicting concepts and recommending exercises task. The corresponding results demonstrate that the MIER outperforms all the other models. We comprehensively evaluated the MIER model's performance across a range of datasets varying in labeled data quantity. The findings demonstrate that the model consistently achieves outstanding performance and exhibits robustness, even under conditions of limited label availability. • End-to-end cross-type exercise recommendations based on educational videos. • The model can extract essential information from the resource representation. • Integrating information from domain experts is crucial. • Our model achieves more precise and robust results on performance prediction. [ABSTRACT FROM AUTHOR]

As a fundamental research task for intelligent education, knowledge tracing (KT) aims to trace learners' dynamic knowledge states and predict their future performance based on their historical response data. However, learners' cognitive processes cannot be studied in isolation from the context; nevertheless, existing knowledge tracing models mainly use knowledge topology as auxiliary information but do not deeply explore the contextual information of exercises. Thus, an in-depth exploration of multi-layer contextual information in learning scenarios is required, focusing on the cognitive differences among different learners. Furthermore, knowledge tracing models exhibit limited interpretability compared with cognition diagnosis methods integrating educational priors. To address these issues, we propose a multi-layer context-aware deep knowledge tracing (MLC-DKT) model. Specifically, we first present the multi-layer contextual representation method involving knowledge concepts and exercises. In the knowledge state tracing module, we incorporate the learners' knowledge priors into the attention mechanism to capture the evolution of the learners' knowledge. Then, we develop a calculation method for the similarity effect among the contextual information, which helps estimate the learners' knowledge states. In the learning performance prediction module, we introduce the guessing and slipping factors from the cognition diagnosis model to optimize the MLC-DKT model, further improving the performance of the KT model. Finally, we conduct extensive experiments on real-world datasets, demonstrating that the MLC-DKT model provides improved predictions of learner performance. Moreover, the MLC-DKT model realizes contextual awareness from knowledge concepts and exercises, making the predictions more interpretable. [ABSTRACT FROM AUTHOR]