# Multi-Output Based Hybrid Integrated Models for Student Performance Prediction
## 1 Introduction  
With the rapid development of the Internet, online education has grown quickly, meeting the urgent needs of the public for home-based learning and educational development through media such as the Internet. The core features of online education are mobility, interactivity, intelligence, personalization, and globalization. Online education has significant advantages in promoting the co-construction and sharing of educational resources and promoting educational equity and is a trend that has been widely applied [1]. Currently, online education mostly relies on online learning platforms, such as China's MOOC and SLCP, which provide students with rich learning resources. However, if students do not pay attention to the accumulation of knowledge during their regular study, the failure rate in their final exams will increase, and their learning quality is worrying. In the teacher's evaluation process, both process evaluation and result evaluation are particularly important, especially in online learning where teachers cannot better pay attention to students' learning progress, resulting in poor process evaluation results. Courses that include teaching practice, experiments and homework reflect to some extent whether students have a good learning attitude and ability in the learning process. In this case, it is particularly important to provide a reliable combined process and result-oriented grade prediction method.  
Currently, research on predicting student grades has achieved certain results, but still faces the following issues:  
Grade prediction is usually conducted late, leaving students and teachers with limited time to change study methods or intervene, resulting in teachers only being able to provideguidance to students who are struggling, but are unable to give more attention to high-performing students.  
Student grade prediction is usually a single-output and results-oriented prediction, such as only predicting final grades, which cannot predict students' learning progress at different stages throughout the semester. This lack of information makes it difficult for teachers to accurate and effectively intervene and provide guidance to students.  
To address these issues, this study proposes a multi-output prediction method to predict students' stage performances from the 7 th week to the end of the semester, including midterm grades, homework grades, experiment grades, and final grades. These grades can more fully reflect students' learning progress.  
The main contributions of this study are reflected in the following two aspects:  
The time for grade prediction was moved up to the 7 th week, providing teachers with more time for teaching intervention and enabling personalized learning for students.  
Additionally, more attributes could now be predicted, including homework, mid-term, experiment, and final exam scores, facilitating the teachers' understanding of students' learning status and integrating process and outcome evaluations.  
The organization of this study is as follows. First, related research is introduced. Second, the framework and experimental process for predicting student grades are detailed. Next, the experimental results are presented, followed by a summary of the study.