Journal of Applied Science and Engineering

Published by Tamkang University Press


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Hai Shou1 and Ying Lu2This email address is being protected from spambots. You need JavaScript enabled to view it.

1Academic Affairs Office, Hangzhou Vocational & Technical College, Hangzhou 310018, China

2International Exchange & Cooperation Department, Hangzhou Vocational & Technical College, Hangzhou 310018, China


Received: January 13, 2024
Accepted: April 13, 2024
Publication Date: May 24, 2024

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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In today’s academic landscape, institutions face challenges categorizing individuals by skills, anticipating student performance, and improving test outcomes. Early guidance for students is paramount, directing their efforts towards specific areas to boost academic success. This analytical approach enables educational institutions to mitigate failure rates by leveraging students’ past performance in relevant courses to predict their results in a particular program. Using state-of-the-art approaches, strategies, and tools to enhance the learning environment is where educational data mining comes in. This new product uses data mining and machine learning approaches to educational data to offer useful tools for comprehending students’ learning environments. The paper innovatively integrates novel optimizers, Dingo Optimization Algorithm (DOA) and Dwarf Mongoose Optimization Algorithm (DMO), with Support Vector Classification (SVC), exploring their effectiveness in enhancing predictive capabilities. Focusing on educational contexts, the study improves SVC functionality by showcasing DMO’s superior performance compared to other hybrid models. This research provides valuable insights into the intersection of ML and education, contributing to the understanding of categorizing individuals, predicting student performance, and improving academic outcomes in educational settings. The effectiveness of the models was assessed using four widely used metrics: Accuracy, Precision, Recall, and F1-score. DMO proved to be a practical optimizer when coupled with SVC compared to the other hybrid model. SVDM (SVC+DMO) increased Accuracy, Precision, Recall, and F1-score index values of the SVC (0.909, 0.920, 0.909, 0.906) model in G2 grading after all iterations completed to 0.929, 0.931, 0.929, and 0.927 respectively which is also higher than results of SVDO.

Keywords: Dingo Optimization Algorithm (DOA); Dwarf Mongoose Optimization Algorithm (DMO); G2 grading; Machine learning; Early guidance

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