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

Jing Yu

doi:10.3991/ijet.v16i05.20297

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

Highlights

The development of big data and artificial intelligence (AI) technology has brought changes to people's life style
Online learning behavior refers to the set of learning related behaviors that occur in the network learning environment
Through data mining technology and machine learning methods, some scholars have compared and analyzed the prediction effect of single classifiers and integrated classifiers, established the academic performance prediction model of online learning, and proved that the integrated learning algorithm can be used for the construction of the classification model, [4]

Summary

Introduction

The development of big data and AI technology has brought changes to people's life style. The situation of teaching and learning has changed because of the development of Internet and AI technology. After analyzing the research results in this field, some scholars summarized learning analysis methods into five categories: statistical analysis and visualization, clustering, text mining, relationship mining and prediction [3]. Speaking, the research on data mining and learning analysis started late in China. Most scholars in China have studied the width of learning analysis, and used learning analysis to predict online learning behavior. Through data mining technology and machine learning methods, some scholars have compared and analyzed the prediction effect of single classifiers and integrated classifiers, established the academic performance prediction model of online learning, and proved that the integrated learning algorithm can be used for the construction of the classification model, [4]

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Results

Conclusion