## 1 Introduction
Nevertheless, the research focused on predicting student learning performance in superiority on one of these issues rather than considering them in their entirety. For instance, previous studies proposed a hybrid sampling strategy to improve the model performance with an imbalanced VLE data set [9; 11; 12], while the others focused on machine learning performance by investigating various model architectures such as the decision tree, naive Bayes, and MLP on student learning performance [8; 13; 14]. To address the issues mentioned earlier, an intelligent predictive framework for explainable student performance prediction (ESPP) is proposed in this paper. It is a novel approach that addresses the above issues holistically. First, the proposed framework built a week-wise students' activity data set based on student interaction via clickstream activities in the VLE during the COVID-19 pandemic. After constructing the new week-wise students' activity data set, a hybrid sampling method with a synthetic minority over-sampling technique (SMOTE) and random under-sampling (RUS) was employed to balance the data set. Then, the prediction model was constructed by using a convolutional neural network (CNN) and long short-term memory (LSTM) layers for student performance prediction. By integrating the convolutional and LSTM mechanisms, the proposed deep learning (DL) model could accurately extract the spatiotemporal features of students' activities' data. Finally, the explainability of the best prediction model will be generated by visualizing the students' activity features maps and the feature importance contributing to the model. The importance features are visualized by using the local interpretable model-agnostic explanations (LIME). In summary, the main contributions of our studies are four-fold. (1) Firstly, a new data set for student learning performance prediction was proposed. The data set was student interaction in the VLE collected during the COVID-19 pandemic arranged in a week-wise timely manner. (2) Secondly, the effectiveness of the proposed models was evaluated and compared with baseline models such as logistic regression (LR), support vector machine (SVM), and LSTM when dealing with spatiotemporal features in predicting student learning performance. (3) Thirdly, an insight into enhancing the school environment was introduced by supporting decision makers in implementing early intervention techniques to reduce the failure rate. For instance, the proposed method accurately forecasted student learning performance early in the sixth week with an accuracy of 0.91. This result could help the student improve his or her performance as early as possible. (4) Finally, an explainable DL model was provided by visualizing and analyzing individual predictions, the importance of features, and identifying typical predictions. This explainable instrument enables human and DL model interactions to intervene in the student failure rate. Furthermore, the teachers and students can focus on which features contribute to the poor performance. Thus, these assessments could be used to enhance the user experience in the VLE. After a brief introduction, this paper is structured as follows. Section 2 summarizes a review of the present literature on student learning performance prediction and explainable machine learning. Then, the data set and proposed methodologies are explained in Section 3. Section 4 presents the findings. Finally, Sections 5 and 6 discuss the results and summarize the current work, respectively.