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Differential Classification Method in Different Teaching Models of Accounting Courses Based on Naive Bayesian Classification Algorithm
ARTICLE

, Wenzhou Business College, Wenzhou, China

iJET Volume 14, Number 8, ISSN 1863-0383 Publisher: International Association of Online Engineering, Kassel, Germany

Abstract

Accounting is an important management discipline with strong theoretical foundation and practical operation. Due to the differences between individuals in the process of learning, the mastery of the subject is different. This requires teachers to implement differential teaching from the differences in student personality in the process of teaching. However, when teachers use the concept of difference teaching to teach, the classification of students' differences is mostly calculated by manual quantification such as records, tests, surveys, etc. This kind of measurement and qualitative method not only wastes manpower, but also has personal subjectivity, blindly relies on individual subjective judgment to judge students' advantages and interests, and has accuracy and scientificity. This requires research on students' differential classification methods. Therefore, this paper proposes a student classification method based on naive Bayesian algorithm. It constructs a classifier based on historical data, and then uses a well-structured and stable classifier to classify the actual pre-classification objects, and actually applies it to the teaching of accounting courses, realizing the difference in the teaching process. Provide data support for future differential teaching research. The results show that the naive Bayesian classification algorithm can be used to analyze the difference in personality and learning of students. Presupposition and generative teaching objectivesand students improve their self-awareness to better promote self-development.

Citation

Ou, X. (2019). Differential Classification Method in Different Teaching Models of Accounting Courses Based on Naive Bayesian Classification Algorithm. International Journal of Emerging Technologies in Learning (iJET), 14(8), 38-51. Kassel, Germany: International Association of Online Engineering. Retrieved December 8, 2019 from .