MOOC Learning Behavior Analysis and Teaching Intelligent Decision Support Method Based on Improved Decision Tree C4.5 Algorithm

Qiang Wu


to better carry out Massive Open Online Courses (MOOC) teaching evaluation and improve teaching effect, firstly, a teaching decision support system with evaluation function is designed by analyzing the actual situation of the college. Secondly, the decision tree data mining algorithm is introduced in the subsystem of student score analysis and evaluation. Finally, the decision tree model of student score analysis evaluation is constructed according to the decision tree algorithm. Through the practical exploration of applying the decision tree algorithm to the MOOC teaching evaluation management system of higher vocational colleges, it is found that the application of data mining technology to the construction of digital campus is not only reflected in the theoretical feasibility, but also reflected in its technical feasibility.


decision tree C4.5 algorithm; MOOC learning behavior; intelligent decision support

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Copyright (c) 2019 Qiang Wu

International Journal of Emerging Technologies in Learning (iJET) – eISSN: 1863-0383
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