Title: Evaluation method for the effectiveness of online course teaching reform in universities based on improved decision tree

Authors: Lin Yang; Haiqing Zhang

Addresses: Xi'an Technological University, Xi'an, 710021, Shan'xi, China ' School of Optoeletronic Engineering, Xi'an Technological University, Xi'an, 710021, Shan'xi, China

Abstract: Aiming at the problems of long evaluation time and poor evaluation accuracy of existing evaluation methods, an improved decision tree-based evaluation method for the effectiveness of college online course teaching reform is proposed. Firstly, the teaching mode of college online course is analysed, and an evaluation system is constructed to ensure the applicability of the evaluation method. Secondly, AHP entropy weight method is used to calculate the weights of evaluation indicators to ensure the accuracy and authority of evaluation results. Finally, the evaluation model based on decision tree algorithm is constructed and improved by fuzzy neural network to further optimise the evaluation results. The parameters of fuzzy neural network are adjusted and gradient descent method is used to optimise the evaluation results, so as to effectively evaluate the effect of college online course teaching reform. Through experiments, the evaluation time of the method is less than 5 ms, and the evaluation accuracy is more than 92.5%, which shows that the method is efficient and accurate, and provides an effective evaluation means for the teaching reform of online courses in colleges and universities.

Keywords: improving decision trees; online courses; teaching reform; impact assessment.

DOI: 10.1504/IJBIDM.2024.140905

International Journal of Business Intelligence and Data Mining, 2024 Vol.25 No.3/4, pp.409 - 423

Received: 30 Aug 2023
Accepted: 28 Feb 2024
Published online: 03 Sep 2024 *

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