电路分析MOOC后台数据分析与挖掘

Data Analysis and Mining of MOOC Data From Circuit Analysis Course

为了发现MOOC课程学习者的学习规律和趋势,该文利用电子科技大学电路分析基础MOOC课程后台数据,从参与者类型、课程各知识点关联分析、学习效果预测等方面,进行了详细的数据挖掘与分析。分析结果表明:学习者参与测试和讨论次数越多,则获得MOOC证书的可能性越大;"基尔霍夫定理和参考方向"知识点的掌握程度与课程各单元测试成绩之间存在着很强的关联性;利用各知识点和单元测试成绩,建立的线性模型,可以很好地预测学习效果;与其他MOOC课程一样,该电路分析基础课的MOOC也具有讨论积极性欠缺的问题。该文的数据挖掘工作为进一步提升学习效果、优化MOOC课程结构提供了重要的依据。

In order to find the learning rules and trends of the MOOC course learners,this paper uses the background data of the MOOC course of the circuit analysis foundation of the University of Electronic Science and Technology of China,and makes detailed data mining and analysis from the types of participants,the correlation analysis of each knowledge point in the course,the prediction of the learning effect and so on.The types of participants,the association analysis of different topics in the course,as well as the anticipation of the final testing grades are addressed in the paper.Through the data mining work,the following interesting conclusions can be reached.First,the more the learners participate in the test and discussion,the higher the possibility of obtaining the MOOC certificate.Second,there is strong association between the mastery of the “Kirchhoff theorem test and the reference direction” knowledge point and the unit test results.Third,it can predict the learning effect very well like other MOOC courses,which also lack the enthusiasm to discuss.The data mining in this paper provides an important basis for further enhancing the learning effect and optimizing the structure of MOOC course.