Title: Prediction method of college students' achievements based on learning behaviour data mining

Authors: Haiqing Zhang; Chao Zhai

Addresses: Freshmen College of Xi'an Technological University, Xi'an, 710021, Shan'xi, China ' Freshmen College of Xi'an Technological University, Xi'an, 710021, Shan'xi, China

Abstract: This paper proposes a method for predicting college students' performance based on learning behaviour data mining. The method addresses the issue of limited sample size affecting prediction accuracy. It utilises the K-means clustering algorithm to mine learning behaviour data and employs a density-based approach to determine optimal clustering centres, which are then output as the results of the clustering process. These clustering results are used as input for an attention encoder-decoder model to extract features from the learning behaviour sequence, incorporating an attention mechanism, sequence feature generator, and decoder. The characteristics derived from the learning behaviour sequence are then used to establish a prediction model for college students' performance, employing support vector regression. Experimental results demonstrate that this method accurately predicts students' performance with a relative error of less than 4% by leveraging the results obtained from learning behaviour data mining.

Keywords: learning behaviour; data mining; college students; achievement prediction method; K-means; support vector regression.

DOI: 10.1504/IJBIDM.2024.140885

International Journal of Business Intelligence and Data Mining, 2024 Vol.25 No.3/4, pp.253 - 270

Received: 13 Jul 2023
Accepted: 16 Nov 2023
Published online: 03 Sep 2024 *

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