Vol.15, No.1, February 2026.                                                                                                                                                                          ISSN: 2217-8309

                                                                                                                                                                                                                        eISSN: 2217-8333

TEM Journal

TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS

Association for Information Communication Technology Education and Science

Enhancing Prediction of Student Performance Based on Semi-Supervised Data Augmentation Approach

Huong Nguyen Thi Cam, Dinh Anh Tuan Tran

© 2026 Dinh Anh Tuan Tran, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)

Citation Information: TEM Journal. Volume 15, Issue 1, Pages 678-687, ISSN 2217-8309, DOI: 10.18421/TEM151-64, February 2026.

Received: 23 March 2025.
Revised: 18 September 2025.
Accepted: 03 October 2025.
Published: 27 February 2026.

Abstract:

The problem of students dropping out of school is complicated, especially for information technology majors. One of the critical goals of contemporary institutions is to deliver quality education while simultaneously lowering academic failure rates. A university’s early identification of low-performing students may improve their academic performance, eliminate academic delays, and lessen the likelihood of academic failure. This study proposed a semi-supervised learning strategy to enhance the dataset for maximum accuracy in forecasting student dropouts. The proposed approach was evaluated and compared to various well-established methods, such as support vector machine (SVM), K-nearest neighbors (KNN), ensemble, and bilayer neural network (BNN). During a single academic year, the dataset was collected from the student management system at a private university. The dataset included grade point averages of 13 courses, the job status of students’ parents, and the geographical locations of the students enrolled in the computer technology program. The study’s findings suggest that when there is a shortage of appropriate training data, dataset improvement may enhance the classifier accuracy. According to the results of the classification technique, the proposed methodology attained a maximum degree of accuracy of approximately 98%.

Keywords – Classification, machine learning, semi-supervised learning, student’s dropout, dataset augmentation.

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