Title: Practical analysis of digital technology of electronic education sharing platform in higher vocational education

Authors: Yuchen Song; Yi Chen

Addresses: School of Arts Design, Nanjing Vocational University of Industry Technology, Nanjing, 210000, China ' School of Arts Design, Nanjing Vocational University of Industry Technology, Nanjing, 210000, China

Abstract: The large amount of shared resources has brought difficulties to learners' choices, making personalised learning difficult. Therefore, the characteristics of learning data related to electronic education platforms were analysed in the experiment. A student performance monitoring model was constructed through improved deep confidence networks (DBNs), linear regression, and other methods, and student performance was evaluated. Through experiments, in the loss performance test of student performance monitoring model, the improved DBN model has better convergence effect and loss performance than BP and DBN models. In the prediction of student performance, the prediction accuracy of the improved DBN model is 0.956, while that of BP model and DBN model is 0.765 and 0.864, respectively. The comprehensive performance of the improved DBN model is the best. The research content has important reference value for Digital transformation of higher vocational education.

Keywords: learning characteristics; electronic education sharing platform; ESP; depth confidence; individual learning; test.

DOI: 10.1504/IJCSYSE.2025.149204

International Journal of Computational Systems Engineering, 2025 Vol.9 No.2/3/4, pp.177 - 186

Received: 26 Apr 2023
Accepted: 17 Jun 2023
Published online: 20 Oct 2025 *

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