Title: A method for evaluating the quality of college curriculum teaching reform based on data mining

Authors: Xuepeng Huang

Addresses: Hubei University of Police, Hubei, 430034, China

Abstract: In order to improve the evaluation effect of current university teaching reform, a new method for evaluating the quality of university course teaching reform is proposed based on data mining algorithms. Firstly, the optimal data clustering criterion was used to select evaluation indicators and a quality evaluation system for university curriculum teaching reform was established. Next, a reform quality evaluation model is constructed using BP neural network, and the training process is improved through genetic algorithm to obtain the model weight and threshold of the optimal solution. Finally, the calculated parameters are substituted into the model to achieve accurate evaluation of the quality of university curriculum teaching reform. Selecting evaluation accuracy and evaluation efficiency as evaluation indicators, the practicality of the proposed method was verified through experiments. The experimental results showed that the proposed method can mine teaching reform data and evaluate the quality of teaching reform. Its evaluation accuracy is higher than 96.3%, and the evaluation time is less than 10ms, which is much better than the comparison method, fully demonstrating the practicality of the method.

Keywords: data mining; university courses; teaching reform; quality evaluation.

DOI: 10.1504/IJBIDM.2024.140882

International Journal of Business Intelligence and Data Mining, 2024 Vol.25 No.3/4, pp.323 - 335

Received: 01 Aug 2023
Accepted: 16 Nov 2023
Published online: 03 Sep 2024 *

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