Title: Evaluation method of English course students' online learning effectiveness based on data mining

Authors: Shuyu Li

Addresses: School of Culture, Tourism and International Education, Henan Polytechnic Institute, Nanyang, 473000, China

Abstract: To improve the quality of online course teaching and solve the problem of poor online learning effectiveness of existing methods for students, taking English courses as an example, research on evaluating the online learning effectiveness of English course students based on data mining technology is carried out. The article first constructs an online learning dataset for English course students and performs preprocessing such as cleaning and outlier removal. Then, based on the K-means algorithm, data mining is completed to establish an evaluation index system for the online learning effectiveness of English course students. The improved analytic hierarchy process (AHP) is used to assign weights to the indicators, and the weighted sum of the evaluation index weights is used to obtain the students' online learning effectiveness scores. Finally, the progressiveness of the proposed method is verified by experiments. The experimental results show that after applying this method, the within-group difference of the indicator data has never exceeded 3.15, and the inter-group difference of the indicator data has always been around 5.5. The maximum evaluation time during the experiment is only 12.1 seconds, effectively realising the design expectations.

Keywords: online learning data; data feature mining; K-means algorithm; evaluation of learning effectiveness; indicator weight.

DOI: 10.1504/IJITM.2025.151548

International Journal of Information Technology and Management, 2025 Vol.24 No.3/4, pp.281 - 295

Received: 16 Oct 2024
Accepted: 07 Feb 2025
Published online: 05 Feb 2026 *

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