Title: A method for evaluating the learning effectiveness of MOOC English online education based on fuzzy clustering decision tree

Authors: Xiaoxia Yu

Addresses: College of Foreign Languages, Liaodong University, Dandong, 118000, Liaoning, China

Abstract: At present, the evaluation accuracy of online education learning effectiveness is low and the evaluation effect is poor. Therefore, this paper proposes a MOOC English online education learning effectiveness evaluation method based on fuzzy clustering decision tree. Firstly, determine the principles for constructing the MOOC English online education learning effectiveness evaluation system, and then use fuzzy clustering algorithm to determine the evaluation indicators. Finally, using the decision tree ID3 algorithm, calculate the attribute and information gain of evaluation indicators, determine the weight of evaluation indicators using the entropy method, construct a fuzzy evaluation matrix, and achieve learning effectiveness evaluation. Through experiments, it has been proven that the coverage rate of the evaluation indicators proposed in this article is always above 90%, and the correlation degree of the evaluation results is always above 0.88. The accuracy of the evaluation is high, and the evaluation effect is good.

Keywords: fuzzy clustering decision tree; online education; learning effectiveness; evaluation matrix; entropy method.

DOI: 10.1504/IJCEELL.2025.143798

International Journal of Continuing Engineering Education and Life-Long Learning, 2025 Vol.35 No.1/2, pp.46 - 61

Received: 22 Jan 2024
Accepted: 09 Sep 2024
Published online: 07 Jan 2025 *

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