摘要
针对传统数据库学习可视化程度低,有效提高学习效率,构建了学习行为大数据可视化的网络数据库学习方法。分析学习行为具体特征,结合贝叶斯理论按学习资源归类可视化数据;设定相关学习变量,观测变量,分别将正确率、错误率、所学知识难度、遗忘概率及状态概率等特征作为可视化函数,通过对学习行为数据采集和储存、分析学习行为及建立可视化模块,建立大数据可视化的网络数据库,充分掌握学习者学习行为情况,随后设定数据库学习评定指标函数;仿真结果表明,所提方法学习结果准确性高和平均任务完成效率都有较大提高,学习者的学习能力有显著提升,方法可行性更高。
Due to low visualization of traditional database learning methods, a network database learning method based on big data visualization of learning behavior was presented.The specific feature of learning behavior was analyzed, and the visual data was classified by learning resources and Bayesian theory.Some relevant learning variables and observation variables were set up.Meanwhile, the correct rate, error rate, knowledge difficulty, forgetting probability and state probability were taken as visualization functions.Through the data collection and data storage of learning behavior, the learning behavior was analyzed and the visualization module was constructed.Furthermore, the visual network database of big data was built to fully master the learner’s learning behavior, and then the database learning evaluation index was set up.Simulation results show that the accuracy and the average efficiency of task completion are greatly improved by the proposed method.Meanwhile, the learner’s learning ability is significantly improved, so the feasibility of the method is higher.
作者
蒙芳
翟建丽
MENG Fang;ZHAI Jian-li(Huali College,Guangdong University of Technology,Guangzhou Guangdong 511325,China)
出处
《计算机仿真》
北大核心
2021年第9期216-220,共5页
Computer Simulation
基金
2019年广东省高等教育教学改革项目(20191206)
2019年广东工业大学华立学院校级课题(广工大华立院教字[2019]32号)。
关键词
学习行为
大数据可视化
贝叶斯理论
网络数据库
Learning behavior
Big data visualization
Bayesian theory
Network database
