摘要
传统面向高校智慧教育的数据分析平台难以从海量智慧资源中准确分析学生学习行为,导致在面向试题的难度预测中,存在准确率低的问题。针对上述问题,开展面向高校智慧教育的大数据分析研究工作,给出智慧教育体系架构的构成以及数据分析平台,利用Hadoop技术对智慧教育资源进行分析与处理,采用数据挖掘算法并结合云计算技术深入分析和解释学生学习行为数据的采集、汇聚,获取学生学习行为的隐性和显性行为,评估教育质量,预测学生日后学习表现,建立学生认知模型与可视化图表,把得到的数据智能融入智慧教育体系架构。将该体系架构应用于在线教育系统提供的答题数据,预测英语阅读试题难度。测试结果表明,试题难度评估预测性能较好。
It is difficult to accurately analyze the students′learning behavior from massive intelligence resources in the traditional data analysis platform for college wisdom education,resulting in the low accuracy problem in the difficulty prediction of test-oriented questions.Therefore,the big data analysis research is carried out for college wisdom education.The composition of the intelligent education system architecture and the data analysis platform are given.The Hadoop technology is used to analyze and process the wisdom education resources.The data mining algorithm in combination with the cloud computing technology is used to deeply analyze and explain the collection and convergence of student learning behavior data.The implicit and explicit behaviors of students′learning are obtained to evaluate the education quality,predict students′future learning performance,and establish the student cognitive model and visualization chart.The acquired data is intelligently integrated into the wisdom education system architecture.The system architecture is applied to the question-answering data provided by the online education system,so as to predict the difficulty of English reading test.The test results show that the prediction performance of the test difficulty evaluation is good.
作者
沈贵庆
SHEN Guiqing(Northwest A&F University,Yangling 712100,China)
出处
《现代电子技术》
北大核心
2019年第4期97-100,共4页
Modern Electronics Technique
基金
陕西省社科基金项目(2017R011)~~
