Design And Implementation Of Online Education Analysis Platform Based On Hadoop

With the rapid development of digital technology and the continuous updates of various terminal devices,people’s ways of life and work have undergone revolutionary changes.In this context,numerous online education and training institutions have emerged like mushrooms after rain,providing people with more convenient learning methods and opportunities to improve their skills.Currently,the amount of data involved in the online education industry has reached a massive level.Only a large online education platform may generate millions to tens of millions of student learning data and teacher course data every day,as well as millions of online testing and exam data and tens of millions of course sales data.As the online education industry develops rapidly and the user base continues to expand,the scale and complexity of the data also continue to increase.The challenges that the online education industry needs to address include how to efficiently collect,store,process,and analyze these massive data.However,traditional online education analytics platforms have various limitations and deficiencies,including insufficient data processing and analysis capabilities,as well as issues with data security.Due to a lack of coordination between different departments,data is difficult to share,resulting in a serious data silo phenomenon.In addition,most online course websites fail to fully utilize student behavior data,lack targeted personalized course recommendations,and further affect student learning effectiveness and experience.To address these issues,measures should be taken to improve the technical capabilities of online education analytics platforms,strengthen the integration and management of massive data,and optimize course recommendation algorithms to achieve personalized recommendations and improve student learning effectiveness and experience.The data in the online education industry involves multiple aspects such as data volume,complexity,and value.To efficiently process and analyze massive data,this thesis utilizes various technologies and methods,including data integration and transformation,standardization,cleaning and deduplication,and visualization.Combining with the actual business requirements of the online education industry,this thesis designs and develops an online education analytics platform based on Hadoop.Additionally,by building a course recommendation system and optimizing the course recommendation algorithm,the utilization rate of massive data and the level of personalized service for institutions are further improved.The main research content and work focuses of this thesis are outlined below:(1)Research the impact of massive data on the analysis of online education,propose improved recommendation algorithms for educational data analysis,increase the utilization rate of massive data,and provide a better user experience for online education analysis platforms.(2)Design and implementation of an online education analysis platform.Design and construct an educational data warehouse based on Hadoop,which is built on a scalable distributed computing framework to achieve efficient collection,storage,cleaning,and analysis of massive data.Additionally,a data visualization module is provided to enable users to intuitively understand the changing trends and related relationships of the data.(3)Testing and summarization of online education analytics platform.We provided a complete testing report,including functional and non-functional testing of the platform,to ensure that it can meet the needs of processing and analyzing massive data.