Academic Performance Prediction Method of Online Education using Random Forest Algorithm and Artificial Intelligence Methods

Abstract

In order to improve the teaching quality of online education, the prediction method of students' online academic performance has been studied. First, the learning analysis, artificial intelligence (AI) and other related theoretical concepts are analyzed and introduced. Then, the decision tree of single classification algorithm and the random forest (RF) of ensemble learning algorithm are analyzed, and the academic performance prediction model of online education is constructed by RF algorithm. Finally, the data of education platform is used for empirical analysis to verify the reliability and practicability of the academic performance prediction algorithm of online education. The connotation of learning analysis, the role and elements of learning analysis in the learning process are introduced. The algorithm principle of RF and decision tree is analyzed. By using the idea of information entropy and discretization, the continuous variables are processed to improve the fitting degree of the algorithm. The model is evaluated by empirical analysis, and the test accuracy of several different algorithms is compared. It is found that the prediction accuracy of the RF algorithm is more than 90%, which shows that the prediction method can help teachers and students to carry out better teaching and learning activities, so as to better improve students' ability to master knowledge. It is hoped that the result can provide some reference for the management of students' learning behavior and the optimization of teachers' teaching strategies in online learning activities

Artificial Intelligence-Based Online Education System for University Music

“…It is used to record the courseware information uploaded by teachers, mainly including the storage of visible number, producer, applicable courses, number of people, and other basic information. Table 10 shows the specific contents of the courseware form [14].…”

Framework of Artificial Intelligence Learning Platform for Education

“…Artificial intelligence refers to technology that simulates human intelligence and behavior to think like humans and imitates human actions (Anagnostopoulou et al, 2020;Maneehaet & Wannapiroon, 2019;Tang & Hai, 2021;Yu, 2021), which are developed based on working principles and incoming technology. Moreover, it helps in working or making decisions instead of human intervention and working wisely (O'Brien, 2020).…”

Research on Abnormal Behavior Prediction by Integrating Multiple Indexes of Student Behavior and Text Information in Big Data Environment

“…Nie et al [43] aimed at the problem of precise poverty alleviation in colleges and universities, based on student behavior data, combined with the time series characteristics of college data, extracted the multidimensional characteristics of students' basic information and behavior data and proposed a CW-LSTM algorithm based on deep learning theory for prediction. Yu et al [44] analyzed the decision tree of the single classification algorithm and the random forest (RF) of the ensemble learning algorithm and constructed an online student achievement prediction model by using the RF algorithm. Xu et al [45] proposed a novel hybrid IDA-SVRbased model to predict student performance, it is an improved decision algorithm (IDA) to optimize support vector regression (SVR).…”