Study On College Students’ Online Learning Behavior Based On Data Mining Technology

In the 21 st century,online education has become a new educational model based on the development of Internet technology.Especially in the post epidemic era,focusing on online learning behavior research and developing and solving online learning resource development,learning effect evaluation,and other issues is also receiving increasing attention.Therefore,data mining analysis of online learning behavior and its application to college students’ online learning systems have important practical significance and application value.Focusing on this research topic,firstly,this thesis completes the collection,mining,and analysis of college students’ online learning behavior data based on the exploration and improvement of association rules and sequential pattern mining algorithms;Secondly,apply data mining technology to personalized learning systems;Finally,optimize the mechanism and learning experience of online learning for college students.Specifically,the research content includes(1)For the association rule and sequential pattern mining methods of learning behavior mining,research and comparison of two kinds of algorithms and optimization performance.The analysis results show that the improved algorithm performance has been significantly improved.(2)Aiming at the problem of college students’ online learning behavior,we completed the construction of a mining model based on college students’ online learning behavior,and applied the improved algorithm to mine and analyze college students’ online learning behavior data,focusing on data preprocessing and data conversion in the database,as well as mining the correlation between college students’ learning behavior,learning sequence patterns,and learning effects,And interpret and evaluate the mining results.(3)The construction and module implementation of personalized learning system,including the design of the platform,data collection,preprocessing and other contents.Finally,the system is tested and evaluated to verify the feasibility.