Title: A method for classifying and mining online teaching data in universities based on decision tree algorithm

Authors: Fei Wang; Xiaoyan Wu

Addresses: School of Continuing Education, Henan Polytechnic, Nanyang City, Henan Province, 473000, China ' School of Mechanical Engineering, Henan Polytechnic College of Industry, Nanyang City, Henan Province, 473000, China

Abstract: In response to the problems of low execution efficiency and high probability of multi-classification loss in the classification mining of online teaching data in universities, this paper proposes a decision tree algorithm based method for classification mining of online teaching data in universities. This method calculates multi-class losses through an unbiased risk estimator and minimises the difference between real and non-real labels for data pre-processing. Then, based on the complexity of experience, the degree of feature fitting is considered to determine the set of feature data, and redundant features are removed from the perspective of two-dimensional real space for feature extraction. Finally, use decision tree algorithm for classification mining. The experimental results show that this method improves execution efficiency and reduces the risk of data loss.

Keywords: decision tree algorithm; online teaching data in universities; classification mining; multi-classification loss; function space.

DOI: 10.1504/IJCEELL.2025.143801

International Journal of Continuing Engineering Education and Life-Long Learning, 2025 Vol.35 No.1/2, pp.171 - 186

Received: 18 Jan 2024
Accepted: 09 Sep 2024
Published online: 07 Jan 2025 *

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