## 1 Introduction  
The World Health Organization (WHO) has advised all nations to restrict face-to-face contact to prevent the transmission of COVID-19. This recommendation applies to the formerly conducted classroom education, which has transformed into online teaching and learning through a virtual learning environment (VLE) such as Moodle [1, 2]. The abrupt transition from classroom to online teaching and learning created a sharp rise in the student failure rate caused by the behavior problems and their interactions in the VLE, for instance, some students lack of their ability in accessing learning material, assignment submission, and interaction during a discussion in the VLE. Assisting students in engaging with the VLE system is one of the critical factors for graduating from online courses [3]. The lack of interaction with classmates and teachers is one of the obstacles in this learning transition [4]. Diminished engagement due to a quick transition to online learning during a pandemic was another challenge [5]. Moreover, the investigation of student access to course materials during digital education was reported in [6]. Then, the effectiveness of a distance learning system was assessed using a comprehensive methodology in [7]. Therefore, early prediction of student learning performance and analysis of initial student behavior in the VLE are crucial to reduce the high failure rate in online courses during the pandemic. The advancement of machine learning technology has received widespread attention for forecasting student performance in earlier weeks of the teaching and learning process in the VLE. The VLE records student interaction data such as course view, assignment submission, resource view, quiz, and discussion. This VLE student data interaction was employed to develop predictive models for various reasons, including predicting student performance. For instance, a multi-layer perceptron (MLP) architecture was proposed to predict student learning performance using the VLE database [8]. This early prediction scheme enables teachers to intervene with an at-risk student with a high failure rate in the online course. Furthermore, the machine learning analytics model can provide teachers with statistical insight to aid teaching and learning. Nevertheless, existing research on student learning performance prediction only focuses on improving model prediction performance. This research goal will suffer from the weakness of temporal feature challenges and imbalanced data set distribution, resulting in poor performance and model prediction explainability in the prediction scenario of early student learning performance. Firstly, the temporal feature challenges refer to a week-by-week student learning performance prediction. For instance, the model could predict the student learning performance as early as the sixth week within 16 weeks of the teaching and learning process. However, the research on student performance prediction is ineffective without the student's activity data set captured in a whole week [8; 9]. Secondly, the VLE data set has a highly imbalanced class data distribution between the positive and negative classes. Imbalanced class data distribution represents a data set in which some classes have a significantly larger sample size than others. For instance, the number of samples for failed students from the course was less than 3% compared to those who passed the course. However, a model developed with an imbalanced label data distribution may significantly degrade the prediction performance [9; 10]. Finally, as for model prediction explainability, the output of model prediction only showed the probability score of students passing or failing. This probability score is complex for the teacher to explain the results to his or her students. The model needs to explain its prediction results based on the importance of student engagements in the VLE. Thus, allowing the teacher to intervene based on the model explainability results is essential.