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A case study of data analysis for educational management

The recent technology development introduces many aspects of difficulty in education management. There are many new technologies and new businesses emerging in a single year. As a result of business sectors require a rapid changing of curriculum from academic institutions. However, the curriculum revision has to be done in a very short time comparing to a large amount of data to be processed, resulting in the teaching materials are often lack behind the business requirements. Education management has to be sensitive and responsive to the constantly changing of technology environment. In this paper, data analytic tools are applied for discovering a new dependence between courses, in order to improve the current sequence of prerequisites. The dependence detection is made of a feature importance technique based on an extra-trees classifier. We also compare various types of classifiers for predicting a grade of a next enrollment using the results of prior studies. The grade prediction is designed to suggest the best study path to match individual talent.