Application of Decision Tree Classification Algorithm in Quality Assessment of Distance Learning in Colleges

Fang Nan; Yanan Li; Jing Zhang; Xuesong Yin; Xintong Cui

doi:10.4108/eetsis.4493

Authors

Fang Nan Qinggong College North China University Of Science And Technology, Tangshan 063000, Hebei, China
Yanan Li Qinggong College North China University Of Science And Technology, Tangshan 063000, Hebei, China
Jing Zhang Qinggong College North China University of Science and Technology, Tangshan 063000, Hebei, China
Xuesong Yin Qinggong College North China University of Science and Technology, Tangshan 063000, Hebei, China
Xintong Cui Qinggong College North China University of Science and Technology, Tangshan 063000, Hebei, China

DOI:

https://doi.org/10.4108/eetsis.4493

Abstract

INTRODUCTION: The quality assessment technology of distance education in colleges and universities, as the critical technology for identifying the quality of distance education in colleges and universities, is conducive to the improvement of the quality of distance teaching and the progress of the existing means and methods of distance education, which makes the means of distance teaching in colleges and universities rich in science.

OBJECTIVES: Aiming at the evaluation methods of higher education institutions, there are problems such as insufficient objectivity and comprehensiveness of the evaluation system, single process, and inadequate quantitative analysis.

METHODS:Proposes a decision tree and intelligent optimization algorithm for the college distance teaching quality assessment method. Firstly, the kernel principal component analysis method is used to carry out dimensionality reduction analysis on the index system of college distance teaching quality assessment; then, the decision tree parameters are optimized through the marine predator algorithm to construct a college distance teaching quality assessment model; finally, the robustness and efficiency of the proposed method are verified through simulation experimental analysis.

RESULTS: The results show that the proposed method improves the accuracy of the assessment model.

CONCLUSION: The problem of insufficient objective and scientific evaluation and low precision of distance teaching quality assessment methods in colleges and universities is solved.

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