基于贝叶斯网络模型的在线学习行为分析

冯广; 潘庭锋; 伍文燕

doi:10.12052/gdutxb.210067

欢迎来到能源电力期刊网！

English Version 期刊登录

您当前的位置：

首页 >

文章列表页 >

基于贝叶斯网络模型的在线学习行为分析

更新时间：2026-05-27

- 基于贝叶斯网络模型的在线学习行为分析
- An Online Learning Behavior Analysis Based on Bayesian Network Model
- 广东工业大学学报 2022年39卷第3期页码：41-48
- 作者机构：
  
  1. 广东工业大学自动化学院,广东,广州,510006
  2. 广东工业大学计算机学院,广东,广州,510006
  3. 广东工业大学网络信息与现代教育技术中心,广东,广州,510006
- 作者简介：
  
  冯广(1973–)，男，教授级高级实验师，博士，主要研究方向为网络控制、机器学习、大数据
  伍文燕(1989–)，女，高级工程师，硕士，主要研究方向为教育信息化、人工智能、大数据，E-mail：wuwy@gdut.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(71671048)
- DOI：10.12052/gdutxb.210067
  中图分类号： G434;TP399
- 网络首发：2022-05-10，
  
  纸质出版：2022
- 稿件说明：
移动端阅览
冯广, 潘庭锋, 伍文燕. 基于贝叶斯网络模型的在线学习行为分析[J]. 广东工业大学学报, 2022, 39(3): 41-48.

Feng Guang, Pan Ting-feng, Wu Wen-yan. An Online Learning Behavior Analysis Based on Bayesian Network Model[J]. 2022, 39(3): 41-48.
冯广, 潘庭锋, 伍文燕. 基于贝叶斯网络模型的在线学习行为分析[J]. 广东工业大学学报, 2022, 39(3): 41-48. DOI： 10.12052/gdutxb.210067.

Feng Guang, Pan Ting-feng, Wu Wen-yan. An Online Learning Behavior Analysis Based on Bayesian Network Model[J]. 2022, 39(3): 41-48. DOI： 10.12052/gdutxb.210067.

摘要

线上线下结合的教学模式是未来教学的一个趋势，每一个学生的学习行为会直接影响学习结果，因此研究学习者学习行为对学习成绩的影响程度是目前的研究重点。目前常见的评价模型存在可信程度较低、可解释性较弱等问题，本文使用基于证据推理的贝叶斯网络(Bayes Network

BN)能够有效地解决这一问题。把方法应用在学习行为分析上，与常用的机器模型和深度学习模型进行比较，表现出更低的误差和更强的可解释性。

Abstract

The combination of online and offline teaching mode is a trend of teaching in the future. The previous researches show that learners’ learning behavior can directly affect the learning results

so the research focuses on studying the influence level of learning behavior on academic performance. For the current common evaluation models

there are still some problems such as low credibility and weak interpretability. In this study

the Bayesian network (BN) based on evidential reasoning can effectively solve this problem. Compared with the commonly used machine model and deep learning model

it shows a lower error rate and a stronger interpretability.

关键词

Keywords

references

JOHNSON M L, SINATRA G M. Use of task-value instructional inductions for facilitating engagement and conceptual change [J]. Contemporary Educational Psychology, 2013, 38(1): 51-63.

LI S, WANG Z X, YU C, et al . Research on analysis framework and measurement index of online learning behavior engagement learning analysis based on LMS data [J ] . Open Education Research, 2016, 22(2): 77-88.

PERAL J, MATE A, MARCO M. Application of data mining techniques to identify relevant key performance indicators [J]. Computer Standards Interfaces, 2017, 50: 55-64.

APARICIO M, OLIVEIRA T, BACAO F, et al . Gamification: a key determinant of massive open online course (MOOC) success [J ] . Information Management, 2019, 56(1): 39-54.

MORRIS L V, FINNEGAN C, WU S S. Tracking student behavior, persistence, and achievement in online courses [J]. The Internet and Higher Education, 2005, 8(3): 221-231.

MA F, LI J. A Research on the classification of learners and patterns of learning behavior based on cluster algorithms under MOOCs’ environment [J]. Journal of Guangdong University of Technology, 2018, 35(3): 18-23.

MOU Z J. Analysis of group characteristics of learning behavior and prediction of learning outcomes in MOOCs environment [J]. China Medical Education Technology, 2020, 34(1): 1-6.

SHEN X Y, LIU M C, WU J W, et al . Towards a model for evaluating students' online learning behaviors and learning performance [J ] . Distance Education in China, 2020, 40(10): 1-8.

WU Q. MOOC Learning behavior analysis and teaching intelligent decision support method based on improved decision tree C4.5 algorithm [J]. International Journal of Emerging Technologies in Learning (iJET), 2019, 14(12): 29-41.

CHEN D X, ZHAN Y Y, YANG B. Analysis of applications of deep learning in educational big data mining [J]. e-Education Research, 2019, 40(2): 68-76.

SUN X, WU N N, ZHANG L, et al . A MOOC dropout rate prediction method based on deep learning [J ] . Computer Engineering Science, 2019, 41(5): 893-899.

HU H, DU S, LIANG J R, et al . Towards a prediction model of learning performance: informed by learning behavior big data analytics [J ] . Distance Education in China, 2021, 41(4): 8-20.

YANG T, TENG S H. Research and application of improved Bayes algorithm for the telecommunication customer churn [J]. Journal of Guangdong University of Technology, 2015, 32(3): 67-72.

CAI R C, ZHEN Q Q, CHEN W, et al . Discovery and validation of causalities among gene mutations based on Bayesian network [J ] . Computer Applications and Software, 2020, 37(7): 22-28.

MOE S J, CARRIGER J F, GLENDELL M. Increased use of Bayesian network models has improved environmental risk assessments [J]. Integrated Environmental Assessment and Management, 2020, 17(1): 53-61.

FANG Y, LIU J H, XIE J Z, et al . Public participation in decision-making of ppp project based on Bayesian network [J ] . Journal of Guangdong University of Technology, 2018, 35(3): 79-86.

张连文, 郭海鹏. 贝叶斯网引论[M]. 北京: 科学出版社, 2006: 175-178.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于聚类算法的MOOCs学习者分类及学习行为模式研究

储能变流器电能质量治理策略研究

多尺度对比嵌入增强的自适应入侵检测模型

双模态迭代交叉注意力融合集成框架

基于AAGV-YOLOX模型的输电线路异物检测研究