Abstract:  To address the issue of the lack of interoperability and data sharing among different information and application systems on campus, we aim to leverage data integration technology to merge diverse educational data sources. We intend to establish a multi-source, heterogeneous education big data mining and application platform. The platform system will utilize the output of artificial intelligence models and the input from a multi- source, heterogeneous education big data mining engine. It will be based on big data mining techniques to analyze and process multiple data sources, including student records, teaching resources, and social behavior information. This will enable functionalities such as educational sign diagnosis, intelligent learning state comparison, analysis of teaching impact factors, identification of potential issues, and prediction of teaching quality trends. Our goal is to scientifically enhance the quality of personalized campus teaching services, objectively assess the teaching proficiency of individuals and teaching teams, assist in analyzing the strengths and weaknesses of teaching individuals and teams, and provide robust support to decision-makers in managing the education system. 

Key words: multi source heterogeneous, education big data, data mining, application platform