Title: Evaluation method of English MOOC teaching quality based on big data mining algorithm

Authors: Jing Han; Jing Ma

Addresses: School of Foreign Languages, Handan University, Handan, Hebei, China ' School of Software, Handan University, Handan, Hebei, China

Abstract: In order to improve the accuracy of English MOOC teaching quality evaluation, an English MOOC teaching quality evaluation method based on big data mining algorithm is proposed. By crawling MOOC platform data and combining TF-IDF model in big data mining algorithm with Zipf's law, relevant indicators affecting the quality of English MOOC teaching are mined from course reviews. In order to improve the efficiency and accuracy of BT-SVM multi-class classification algorithm evaluation, Euclidean distance is used to improve the evaluation of English MOOC teaching quality. The results show that the R²-value of the proposed method is above 0.9, the F1-score can reach 0.98 and the maximum evaluation time is 6.10 s, indicating that the proposed method has high evaluation accuracy and efficiency.

Keywords: teaching quality evaluation; TF-IDF model; Zipf's law; Binary tree support vector machine multi-class classification algorithm.

DOI: 10.1504/IJCAT.2024.146135

International Journal of Computer Applications in Technology, 2024 Vol.75 No.2/3/4, pp.103 - 113

Received: 29 Aug 2024
Accepted: 02 Jan 2025
Published online: 07 May 2025 *

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