%0 Journal Article
%T Study on Online Learning Behavior Analysis and Performance Prediction Based on Improved Random Forest Algorithm
%A Dongxu Liu
%J Creative Education
%P 1527-1535
%@ 2151-4771
%D 2023
%I Scientific Research Publishing
%R 10.4236/ce.2023.148097
%X With
the popularity of online learning, the analysis of student learning behavior
and the prediction of academic performance have become a hot research topic in
the field of education. This study aims to analyze the factors influen<span>cing learning outcomes on online platforms,
extract the feature variable</span>s from online learning behavior, and
construct a weighted random forest model to <span>predict
students¡¯ online learning performance. Additionally, a method for</span> computing feature importance is designed to analyze online learning behavior
based on the significance of different behavioral features. This contributes to
gaining early insights into students¡¯ learning progress, proactively
identifying <span>learning issues</span></span><span style=\"font-family:&quot;\"> and</span><span style=\"font-family:&quot;\"> strategically allocating educational resources,
thus pro</span><span style=\"font-family:&quot;\">viding students with enhanced guidance and support.
%K Online Learning
%K Behavioral Features
%K Personalized Learning
%K Random Forest
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=126906