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Issue: Vol. 1 No. 1 (2026): AJES
Section: Articles
DOI: 10.52714/dthu.ajes.7.1946
Date Published: 06/06/2026
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Huynh-Cam , T.-T., Chen, L. S., & Huynh, K. V. (2026). Factors Influencing the Academic Performance of Freshmen: Insights from a Data-driven Analysis. Asian Journal of Educational Sciences, 1(1), 31-46. https://doi.org/10.52714/dthu.ajes.7.1946

Factors Influencing the Academic Performance of Freshmen: Insights from a Data-driven Analysis

Thao-Trang Huynh-Cam 1,   , Long Sheng Chen2, , Khai Vinh Huynh3
1 Foreign Languages and Informatics Center, Dong Thap University, Vietnam
2 Department of Industrial Engineering and Management, National Taipei University of Technology, Taiwan
3 Office of Quality Assurance, Dong Thap University, Vietnam

Main Article Content

Abstract

Student academic performance has been continuously examined by theorists and practitioners worldwide. Yet, the academic performance of freshmen as newcomers who often face challenges and need assistance has gained little attention from theorists and practitioners. This pilot study uncovers key factors influencing the academic performance of freshmen at a technical and vocational university in Taiwan. Using predictive analytics, this study explored how data-driven methods can enhance students' performance. The analysis utilized the recursive feature elimination (RFE) method for feature selection using two AI-driven models of a Neural Network (NN) and a Decision Tree (DT). The research sample included 1,928 freshmen from a technical and vocational university in Taiwan. The input factor dimensions comprised demographic, socio-economic, and family background variables. The output factor was the grade point average (GPA) for the first term of the academic year 2020/2021. The results showed that among the tested models, the DT surpassed the NN with an accuracy, precision, recall, and F1 of 86.0% (D1) and of approximately 90.0% (D2); and an ROC-AUC of 86% (D1) and 87% (D2). The three factors—students' fathers' careers, major, and the average monthly income of students' parents—had the highest positive and significant impact on freshmen’s academic performance. This work provided AI-driven models serving as an early warning system and identify the strongest predictors of academic performance among freshmen. It is expected to assist educational policymakers in developing proactive measures to increase the number of excellent students and to reduce the number of underperforming/at-risk students at early stages.

Keywords

Academic performance, data-driven analysis, feature selection methods, first-year students, higher education policy, influential factors, predictive models

Article Details

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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