Predicting First-Year Academic Success: The Influence of Socioeconomic and Educational Backgrounds among First-Generation Scholarship Students
Abstract
Objective: This study examines the factors influencing the academic success of first-year students who are first-generation scholarship recipients (FGCS) at Universitas X, with a focus on the impact of socioeconomic background, parental education, and entrance exam scores. Uniquely, this study combines predictive modeling with a detailed analysis of regional, school, and familial factors, providing a comprehensive early-warning system to identify at-risk students and guide targeted interventions. Using a multivariate quantitative approach, data from 511 FGCS recipients from the 2021–2024 cohorts were analyzed. Descriptive statistics reveal significant disparities in academic performance across regions, school types, and study programs. Students from Java and Sumatra achieved the highest average GPAs (3.29 and 3.21, respectively), with state school graduates performing particularly well (average GPA: 3.24). Conversely, students from private schools in Papua and Maluku faced significant academic challenges, with GPAs below the average (2.55 and 2.94). Multiple linear regression modeling, which includes entrance exam scores, predicts first-year GPA with a 6.08% error margin, providing a reliable tool for early detection of academic risk. The regression model shows that entrance exam scores in Bahasa Indonesia and TPA, as well as regional background, significantly influence academic performance. Specifically, students from Sulawesi (β = 0.203) and Java (β = 0.175) are predicted to achieve the highest GPAs, followed by students from Nusa Tenggara (β = 0.118), Sumatra (β = 0.114), Kalimantan (β = 0.101), Papua (β = 0.068), and Maluku (β = 0.059). These results indicate clear regional disparities in predicted academic outcomes. The study concludes with evidence-based policy recommendations for targeted academic support programs and interventions to enhance the academic success of FGCS, particularly those from underprivileged regions and schools.
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References
Alyahyan, E., & Düştegör, D. (2020). Predicting academic success in higher education: literature review and best practices. International Journal of Educational Technology in Higher Education, 17(1), 3. https://doi.org/10.1186/s41239-020-0177-7 DOI: https://doi.org/10.1186/s41239-020-0177-7
Arifuddin, D., Kusrini, K., & Kusnawi, K. (2025). Perbandingan Performansi Algoritma Multiple Linear Regression dan Multi Layer Perceptron Neural Network dalam Memprediksi Penjualan Obat. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 5(2), 722–737. https://doi.org/10.57152/malcom.v5i2.1952 DOI: https://doi.org/10.57152/malcom.v5i2.1952
Banjarnahor, E., Belferik, R., Cendana, W., Adi, Y., & Abraham, S. (2025). Analisis Implementasi Support Vector Machine dan Random Forest untuk Prediksi Kategori Indeks Kualitas Udara Jakarta under a Creative Commons Attribution-NonCommercial ShareAlike 4.0 International (CC BY-NC-SA 4.0). 10(1), 2541–1179. https://journal.uin-alauddin.ac.id/index.php/instek DOI: https://doi.org/10.24252/instek.v10i1.56477
Banjarnahor, E., Sibarani, D. P., Wibawanta, B., Sihotang, D. A. G., & Abraham, Y. A. S. (2025). A Machine Learning Approach to Predicting Student Success Through Data Mining of LMS Moodle Activity Data. 2025 4th International Conference on Electronics Representation and Algorithm (ICERA), 233–238. https://doi.org/10.1109/ICERA66156.2025.11086633 DOI: https://doi.org/10.1109/ICERA66156.2025.11086633
Barsegyan, V., & Maas, I. (2024). First-generation students’ educational outcomes: The role of parental educational, cultural, and economic capital – A 9-years panel study. Research in Social Stratification and Mobility, 91, 100939. https://doi.org/10.1016/j.rssm.2024.100939 DOI: https://doi.org/10.1016/j.rssm.2024.100939
Canning, E. A., White, M., & Davis, W. B. (2024). Growth Mindset Messages from Instructors Improve Academic Performance Among First-Generation College Students. CBE—Life Sciences Education, 23(2). https://doi.org/10.1187/cbe.23-07-0131 DOI: https://doi.org/10.1187/cbe.23-07-0131
Farhood, H., Joudah, I., Beheshti, A., & Muller, S. (2024). Evaluating and Enhancing Artificial Intelligence Models for Predicting Student Learning Outcomes. Informatics, 11(3), 46. https://doi.org/10.3390/informatics11030046 DOI: https://doi.org/10.3390/informatics11030046
Fitrianah, D., Dwiasnati, S., H, H. H., & Baihaqi, K. A. (2021). Penerapan Metode Machine Learning untuk Prediksi Nasabah Potensial menggunakan Algoritma Klasifikasi Naïve Bayes. Faktor Exacta, 14(2), 92. https://doi.org/10.30998/faktorexacta.v14i2.9297 DOI: https://doi.org/10.30998/faktorexacta.v14i2.9297
He, M. (2025). A linear regression modeling study of university language education and students’ expressive skills. Journal of Combinatorial Mathematics and Combinatorial Computing, 127a. https://doi.org/10.61091/jcmcc127a-316 DOI: https://doi.org/10.61091/jcmcc127a-316
Hopper, L. N., Yada, F. N., Abdulkareem, O., Jerger Butts, S., & Lacci-Reilly, K. R. (2024). A Picture of Perfect Mental Health: Exploring Barriers and Facilitators to First-Generation College Student Wellness Through Photovoice. Journal of First-Generation Student Success, 1–23. https://doi.org/10.1080/26906015.2024.2415091 DOI: https://doi.org/10.1080/26906015.2024.2415091
Howe Eng, T., Eng, L. S., Ying, L. C., Daneil, I. L. A., & Ting, G. L. C. (2023). An Application of Multiple Linear Regression Analysis to Establish the Relationship between Higher Order Thinking Skills and Affective Domain. International Journal of Academic Research in Business and Social Sciences, 13(5). https://doi.org/10.6007/IJARBSS/v13-i5/16907 DOI: https://doi.org/10.6007/IJARBSS/v13-i5/16907
Ives, J., & Castillo-Montoya, M. (2020). First-Generation College Students as Academic Learners: A Systematic Review. Review of Educational Research, 90(2), 139–178. https://doi.org/10.3102/0034654319899707 DOI: https://doi.org/10.3102/0034654319899707
Iwasaki, M. (2020). Multiple Regression Analysis from Data Science Perspective (pp. 131–140). https://doi.org/10.1007/978-981-15-2700-5_8 DOI: https://doi.org/10.1007/978-981-15-2700-5_8
Juhaidi, A. (2023). Gender, Latar Belakang Sosio-ekonomi Keluarga, dan Prestasi Akademik Mahasiswa (Kasus pada Universitas Islam Negeri Terbesar di Kalimantan). Metta : Jurnal Ilmu Multidisiplin, 3(4), 393–404. https://doi.org/10.37329/metta.v3i4.2853 DOI: https://doi.org/10.37329/metta.v3i4.2853
Kracht, A. G., Doherr, M. G., & Jensen, K. C. (2025). Disparities in veterinary education: a survey comparing first-generation and continuing-generation students in Germany. Frontiers in Veterinary Science, 12. https://doi.org/10.3389/fvets.2025.1595643 DOI: https://doi.org/10.3389/fvets.2025.1595643
LeBouef, S., & Dworkin, J. (2021). First-Generation College Students and Family Support: A Critical Review of Empirical Research Literature. Education Sciences, 11(6), 294. https://doi.org/10.3390/educsci11060294 DOI: https://doi.org/10.3390/educsci11060294
López, M. J., Santelices, M. V., & Taveras, C. M. (2023). Academic performance and adjustment of first-generation students to higher education: A systematic review. Cogent Education, 10(1). https://doi.org/10.1080/2331186X.2023.2209484 DOI: https://doi.org/10.1080/2331186X.2023.2209484
Munir, J., Faiza, M., Jamal, B., Daud, S., & Iqbal, K. (2023). The Impact of Socio-economic Status on Academic Achievement. Journal of Social Sciences Review, 3(2), 695–705. https://doi.org/10.54183/jssr.v3i2.308 DOI: https://doi.org/10.54183/jssr.v3i2.308
Oyedeji, A. O., Salami, A. M., Folorunsho, O., & Abolade, O. R. (2020). Analysis and Prediction of Student Academic Performance Using Machine Learning. JITCE (Journal of Information Technology and Computer Engineering), 4(01), 10–15. https://doi.org/10.25077/jitce.4.01.10-15.2020 DOI: https://doi.org/10.25077/jitce.4.01.10-15.2020
Pancsofar, N., Hall, M., Gouin, K., & Espinal, A. C. (2024). Motivation, Drive, and Pride: Exploring Assets of First-Generation College Students Majoring in Education. Journal of First-Generation Student Success, 1–18. https://doi.org/10.1080/26906015.2024.2432873 DOI: https://doi.org/10.1080/26906015.2024.2432873
Pressimone Beckowski, C. (2025). Navigators in Training: First-Generation Graduate Students in College Student Affairs. Journal of First-Generation Student Success, 5(2), 123–139. https://doi.org/10.1080/26906015.2024.2376761 DOI: https://doi.org/10.1080/26906015.2024.2376761
Ramirez-Montoya, M. S., Morales-Menendez, R., Tworek, M., Escobar, C. A., Tariq, R., & Tenorio-Sepulveda, G. C. (2024). Complex competencies for leader education: artificial intelligence analysis in student achievement profiling. Cogent Education, 11(1). https://doi.org/10.1080/2331186X.2024.2378508 DOI: https://doi.org/10.1080/2331186X.2024.2378508
Rispandi. (2023). Implementasi Metode K-Medoids Untuk Clustring Penerima Bantuan Berdasarkan Normalisasi Data Masyarakat Miskin Dengan Metode Desimal Scaling. Journal of Computing and Informatics Research, 3(1), 141–152. https://doi.org/10.47065/comforch.v3i1.1063 DOI: https://doi.org/10.47065/comforch.v3i1.1063
Spencer, G. A., & Goldstein, S. E. (2025). “We Don’t Know These Answers. We Don’t Know Where to Go”: The Experience of First-Generation College Student Mothers. Journal of First-Generation Student Success, 1–20. https://doi.org/10.1080/26906015.2025.2515485 DOI: https://doi.org/10.1080/26906015.2025.2515485
Sudbrock, C., Uribe-Zarain, X., Glaessgen, T. A., Sottile, J., & Finch, K. K. (2024). Examining Social Capital and First-Generation Student Status: Evidence from a Midwest University. Journal of Research in Technical Careers, 8(2), 32. https://doi.org/10.9741/2578-2118.1155 DOI: https://doi.org/10.9741/2578-2118.1155
Weisen, S., Do, T., Peczuh, M. C., Hufnagle, A. S., & Maruyama, G. (2024). How are first‐generation students doing throughout their college years? An examination of academic success, retention, and completion rates. Analyses of Social Issues and Public Policy, 24(3), 1274–1287. https://doi.org/10.1111/asap.12413 DOI: https://doi.org/10.1111/asap.12413
Copyright (c) 2025 Budi Wibawanta, Evander Banjarnahor (Author)
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