# Multi-Output Based Hybrid Integrated Models for Student Performance Prediction
## 2 Related Work  
Student grade prediction has always been a hot topic in educational data mining. Currently, there have been some achievements in predicting learning outcomes. Machine learning-based grade prediction is usually divided into classification prediction and regression prediction. Classification prediction predicts the grade level, while regression prediction predicts the specific grade value. [PERSON] et al. [2] proposed a new model based on machine learning algorithms to predict the final exam scores of college students. By collecting mid-term scores and student data from departments and colleges at a state university, they classified final exam scores into four categories. After comparing the performance of various algorithms such as random forest, k-nearest neighbors, classification neural network model, and support vector machine, they selected the optimal model to predict students' final exam scores. A random forest and classification neural network model had an accuracy rate of 70-75% and only used three dimensions and three attributes, reducing the difficulty of data collection. [PERSON] et al. [3] proposed an improved IDA-SVR (Support Vector Regression Optimized by Dual Algorithms) algorithm to predict student performance scores, with a mean squared error of 0.0089, which was better than other algorithms such as decision trees and SVR. Their data included 18 attributes in four dimensions: basic information, interests, classroom behavior, and extracurricular behavior, and had diverse data types. [PERSON] et al. [4] predicted English and mathematics scores of 16-year-old Malaysian students using their past academic performance, demographics, and psychological information. They also explored the main factors that affect students' English and mathematics scores and the relationship between them. The experiment showed that decision trees had the best performance in predicting English scores, and Bayesian algorithms had the best performance in predicting mathematics scores, with an accuracy rate of 87.1% and 83.9%, respectively, and found that there was a certain relationship between mathematics and English scores. [PERSON] et al. [5] proposed an end-to-end deep learning model that could automatically extract features from heterogeneous student behavior data to predict learning outcomes. They collected and experimented with daily behavior data from 8228 students, and the experiment showed that the accuracy of this deep learning model in grade prediction was better than traditional machine learning algorithms. In their study, [PERSON] et al. [6] attempted to construct a predictive model of the one-to-one relationship between student online behavior and academic performance using five machine learning algorithms. However, they found it difficult to obtain good predictive results, with the highest accuracy of the predictive model being able to predict 74.7% of students correctly in all mixed courses. There were significant differences in the predictive accuracy of students with different learning outcomes. Students with grades A (90-100) and B (80-89) had a higher predictive accuracy, reaching 80.6% and 85.3%, respectively. However, the highest predictive accuracy for students with grades of C (70-79) or below was only 63%. [PERSON] et al. [7] used a combination of a four-layer stacked long short-term memory (LSTM) network, random forest (RF), and gradient boosting (GB) techniques to predict whether students passed or failed, achieving an accuracy rate of 96% by verifying with the OULAD dataset. [PERSON] et al. [8] designed a multi-algorithm online learning performance prediction framework that integrated neural networks, decision trees, K-nearest neighbors, random forests, and logistic regression algorithms to predict online learning performance. They utilized Weka software to automatically implement the multi-algorithm fusion prediction function and conducted a predictive performance analysis.