The Design And Realization Of Management Dashboard For Polytechnics’personnel Training

Data collecting platform for vocational institutes is the essential part of new evaluation scheme of vocational institutes and one of its features is data-supported decision. Many platforms have been established by individual vocational institutes and some analysis has been done on those platforms. But much of this work is unfocused and incomplete due to the lack of good software support, which confines and weakens the data collecting platform’s contributions.In order to adapt to the status of data analysis and application requirements of a large number of higher vocational talents training for higher vocational talents training, research on Management Dashboard(MD) become more active. Based on Key Performance Indicators(KPI) and MD technology in higher vocational talents training management, the purpose of this paper is to build a complete vocational training MD system. Based on the analysis of the origin of KPI and MD technologies and their applications, the construction of higher vocational talents training system of KPI is performed. The analysis of the requirement for higher vocational talents Training MD system is put forward, and function demand and other non-functional requirements about KPI index, KPI management, data acquisition management, code set management and system management are studied. The whole design of the higher vocational talents training MD system is demonstrated, so that the architecture of browser / server / database is determined. In the detailed design, each function module of the system, data structure and error handling are involved and the system is realized on J2 EE platform. Finally, the function and performance of the system was tested, and test coverage analysis and the defects of the procedures were carried out. The test results show that the function and performance of the system are acceptable.MD development for higher vocational talents training is conducive to promoting the application of data acquisition platform, fully tapping the potential of data mining and achieving the goal of normalization and scientific management are our next focus.