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

Authors

  • Qiang Wu Anhui Jianzhu University

DOI:

https://doi.org/10.3991/ijet.v14i12.10810

Keywords:

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

Abstract


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.

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Published

2019-06-27

How to Cite

Wu, Q. (2019). MOOC Learning Behavior Analysis and Teaching Intelligent Decision Support Method Based on Improved Decision Tree C4.5 Algorithm. International Journal of Emerging Technologies in Learning (iJET), 14(12), pp. 29–41. https://doi.org/10.3991/ijet.v14i12.10810

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Section

Papers