Title: Online and offline hybrid teaching data mining based on decision tree classification

Authors: Yu Cao; Shu-Wen Chen; Hui-Sheng Zhu

Addresses: School of Physics and Information Engineering, Jiangsu Second Normal University, Nanjing 210013, China; Jiangsu Province Engineering Research Centre of Basic Education Big Data Application, Nanjing 210013, China ' School of Physics and Information Engineering, Jiangsu Second Normal University, Nanjing 210013, China; Jiangsu Province Engineering Research Centre of Basic Education Big Data Application, Nanjing 210013, China ' School of Physics and Information Engineering, Jiangsu Second Normal University, Nanjing 210013, China; Jiangsu Province Engineering Research Centre of Basic Education Big Data Application, Nanjing 210013, China

Abstract: In order to overcome the problems of large mining errors and low classification accuracy of traditional teaching data mining methods, a hybrid online and offline teaching data mining method based on decision tree classification is proposed. First of all, the online and offline mixed teaching data is obtained with the help of crawler technology. Secondly, data repair method is adopted to ensure data consistency, and duplicate data values are determined by distance value to complete data pre-processing. Finally, according to the construction of the decision tree, determine the root entropy and leaf entropy of the mixed teaching data, create the root node, attribute list and class list of the mixed teaching data, and complete the online and offline mixed teaching data mining. The experimental results show that the proposed method can effectively reduce the error of data mining, with the error coefficient not exceeding 0.2, and improve the classification accuracy.

Keywords: decision tree classification; online and offline teaching; data mining: crawler technology; root entropy; leaf entropy.

DOI: 10.1504/IJCEELL.2024.140714

International Journal of Continuing Engineering Education and Life-Long Learning, 2024 Vol.34 No.5, pp.489 - 500

Received: 09 Aug 2022
Accepted: 04 Nov 2022
Published online: 02 Sep 2024 *

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