Research On The Application Of Network Teaching Platform Based On Behavior Data Analysis

In the Internet age,great changes have taken place in different walks of life.The learning method based on the network environment has been developed from scratch,and now it has been widely used.A large number of teaching behavior data have been generated on various network teaching platforms.Its value is gradually highlighted.Researchers use a variety of methods to analyze a large number of data generated by the network teaching platform,and the research results are very extensive.In the study of learning behavior,researchers pay more attention to the learning behavior of students on the open learning platform MOOC,but less to the behavior of teachers and students under the network teaching platform.In this study,we choose the behavior of teachers and students on the network teaching platform to analyze,and use Data Mining to extract useful information of the network learning behavior data,so as to find effective learning laws and propose strategies to improve the teaching effect to provide data support for the effective development of hybrid teaching supported by network teaching platform.Most universities in Inner Mongolia carry out public compulsory course teaching through the Erya course provided by the Xuexitong learning platform.Network teaching platform records a lot of behavior data of teachers and students,which can truly reflect the development of network teaching.Under the guidance of Data Mining,this study takes a public compulsory course of network teaching platform of a university in Inner Mongolia in 2019 as the research object,extracts the basic characteristics of learners,characteristics of online teaching behavior,characteristics of learners' learning effect,etc,and constructs new behavior attributes based on these three characteristics data to deeply depict teachers' and students' behaviors.Throughdata mining of teachers and students' network teaching behavior data stored in the network teaching platform,this study is helpful to study students' learning behavior law in theory,understand teaching process and evaluate teaching effect in application,so as to improve teaching efficiency and teaching quality.In the practice of data mining of online teaching behavior,Python is used to design data analysis and processing programs,Statistical Product and Service Solutions and General Sequential Querier are used to analyze the behavior of teachers and students from multiple perspectives,to mine the behavior patterns of teachers and students,to explore the relationship between behavior patterns and learning achievements,to discuss the causes of behavior,and to put forward suggestions for improving online teaching.The research shows that Data Mining and Lag Sequential Analysis are effective methods to analyze online teaching behavior data.The conclusion of the study enriches the research of teaching behavior,which has a certain referential significance for developing network teaching and construction of teaching platform.