The prediction and analysis of students’ academic performance are essential tools for educators and learners to improve teaching and learning methods. Effective predictive methods assist learners in targeted studying based on forecast results, while effective analytical methods help educators design appropriate educational content. However, in actual educational environments, factors influencing student performance are multidimensional across both temporal and spatial dimensions. Therefore, a student performance prediction and analysis method incorporating multidimensional spatiotemporal features has been proposed in this study. Due to the complexity and nonlinearity of learning behaviors in the educational process, predicting students’ academic performance effectively is challenging. Nevertheless, machine learning algorithms possess significant advantages in handling data complexity and nonlinearity. Initially, a multidimensional spatiotemporal feature dataset was constructed by combining three categories of features: students’ basic information, performance at various stages of the semester, and educational indicators from their places of origin (considering both temporal aspects, i.e., performance at various stages of the semester, and spatial aspects, i.e., educational indicators from their places of origin). Subsequently, six machine learning models were trained using this dataset to predict student performance, and experimental results confirmed their accuracy. Furthermore, SHAP analysis was utilized to extract factors significantly impacting the experimental outcomes. Subsequently, this study conducted data ablation experiments, the results of which proved the rationality of the feature selection in this study. Finally, this study proposed a feasible solution for guiding teaching strategies by integrating spatiotemporal multi-dimensional features in the analysis of student performance prediction in actual teaching processes.

Explainable artificial intelligence (XAI) is crucial in education for making educational technologies more transparent and trustworthy. In the domain of student performance prediction, both the results and the processes need to be recognized by experts, making the requirement for explainability very high. The belief rule base (BRB) is a hybrid-driven method for modeling complex systems that integrates expert knowledge with transparent reasoning processes, thus providing good explainability. However, class imbalances in student grades often lead models to ignore minority samples, resulting in inaccurate assessments. Additionally, BRB models face the challenge of losing explainability during the optimization process. Therefore, an explainable student performance prediction method based on dual-level progressive classification BRB (DLBRB-i) has been proposed. Principal component regression (PCR) is used to select key features, and models are constructed based on selected metrics. The BRB’s first layer classifies data broadly, while the second layer refines these classifications for accuracy. By incorporating explainability constraints into the population-based covariance matrix adaptation evolution strategy (P-CMA-ES) optimization process, the explainability of the model is ensured effectively. Finally, empirical analysis using real datasets validates the diagnostic accuracy and explainability of the DLBRB-i model.

Autonomous underwater vehicles (AUVs), sophisticated devices that operate independently in complex underwater environments, are prone to malfunctions. Therefore, fault diagnosis is crucial for maintaining stable operations. The massive amounts of complex multidimensional data generated during AUV operations make it challenging to construct rule bases efficiently. The extended belief rule base (EBRB) offers a solution by employing data-driven approaches. Furthermore, as the data are sequentially generated, online training is more aligned with the practical realities of AUV operations. When processing large amounts of data, the EBRB system generates a substantial number of rules. This process can be very time-consuming. It may trigger the activation of many irrelevant rules, potentially leading to erroneous decisions. In response, a fault diagnosis method for autonomous underwater vehicles based on an online extended belief rule base with dynamic rule reduction (OEBRB-R) is introduced in this paper. This approach involves first establishing an OEBRB model that continually processes new incoming sensor data in real time. Subsequently, it employs a Mahalanobis distance-based k-means clustering technique to dynamically reduce the number of active rules. Finally, evidence reasoning (ER) algorithms are utilized to infer the type of fault present. Through case studies, the proposed method has been empirically validated for maintaining strong accuracy in real-time diagnostics while effectively reducing the model’s spatial complexity.