## 1 Introduction  
With the continuous improvement of online learning platforms, a proliferation of schools has opened up online course teaching, providing students with more flexible learning methods. However, there is a significant problem with the online learning process: due to the lack of face-to-face interaction between instructors and students, it is difficult for instructors to accurately grasp the level of understanding and mastery of course content, which may lead to students' poor performance on exams. Therefore, it is particularly important to capture the hidden information related to student performance from massive educational data collected by online learning platforms. Using this information, students at risk of failing and dropping out of school can be identified and an intervention can be carried out as early as possible to effectively improve their learning status and enhance learning effectiveness [1]. Data mining techniques aid researchers in discovering and understanding latent critical information within massive educational datasets, enabling the identification and prediction of students' performance trends [2]. [PERSON] et al. [3] mined high/active engagement, attendance, and multiple interactions as key factors for students to achieve higher marks on a Massive Open Online Learning(MOOC) platform by using data mining techniques. Such information extraction is not confined solely to the field of data mining but also extends into the domain of learning analytics. With the rise in learning analytics, there has been a growing emphasis on investigating student behaviors and aggregating various methodologies to enhance the comprehension of student behaviors, consequently enabling the prediction of students' academic performance [4]. As a category of data reflecting student behaviors, clickstream data illustrate the pathways students take while navigating one or multiple learning websites on an online learning platform [5]. [PERSON] et al. [6] used data from students' interactions with the virtual learning environment to analyze learning through a neuro-fuzzy system, showing that students who regularly visit the course homepage and actively participate in the teaching and learning activities through evaluation tests are more likely to pass the exams. In addition to clickstream data, students' demographic information and assessment scores have also been verified to be closely correlated with students' academic performance [7; 8]. Considering more dimensions of information beyond students' learning behaviors, modeling and analyzing students' academic performance as a whole can more accurately predict students' academic performance. The early prediction of student performance allows for the proactive identification of at-risk students, enabling instructors to devise additional courses, and assignments, or implement other measures to enhance students' performance before the conclusion of the course. Currently, most research only applies to predicting student performance at the end of the course, which does not allow for timely intervention. Therefore, it is particularly important to predict student performance as early as possible before the end of the course [9]. Furthermore, most studies have predicted student performance (pass, fail) on a dichotomous basis, which would result in students at an excellent level losing their upward mobility and failing to maximize benefits from the course [10]. To promote the sustainable development of online education and provide a basis for teachers to intervene with at-risk students promptly, this paper synthesizes multidimensional data, such as students' learning behaviors, assessment scores, and demographic information, and proposes a prediction model for student performance based on the analysis of multidimensional time-series data (MTAPSP), aiming at efficiently and accurately predicting students' performances in an online learning platform. The main contributions of this study can be summarized in the following three points:  
1.