The Influence of Online Learning Behavior on Learning Performance

Huiming Li


Online education is a significant part of information education. It is an effective way to uncover online learning mechanisms and improve the quality of online teaching by exploiting the behavioral data of online learning platforms for learning performance prediction and analysis. In this paper, we focus on the learner’s learning behavior in an online teaching scenario and explore the predictive effectiveness and impact mechanism of each behavioral feature by building a predictive model based on a machine learning algorithm. Experimental results show that three behavioral characteristics, namely the number of visits to course materials, lecture review time, and assignment, intensively influence learning performance. By comparing various machine learning algorithms, it is found that the random forest algorithm has better prediction results.

Full Text:




  • There are currently no refbacks.

Copyright (c) 2023 Huiming Li

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © SCHOLINK INC.   ISSN 2474-4972 (Print)    ISSN 2474-4980 (Online)