Parental Income and Education as Predictors of Physics Exam Success: A Neural Network and Random Forest Regression Analysis
DOI:
10.29303/jppipa.v11i10.12307Published:
2025-10-25Downloads
Abstract
This study examines the relationship between the socioeconomic status (SES) of tenth-grade students at SMA Negeri 4 Praya and their final physics exam scores for the 2022/2023 academic year. SES indicators include parents' income and education level, collected via qualitative questionnaires and quantitative assessment of physics exam scores. Random Forest Regression and Neural Network techniques were used for analysis. The results showed no significant relationship between SES and physics scores. For parents' education level, Neural Network Regression yielded a Mean Squared Error (MSE) of 323.78 and an R² score of -0.0129, while Random Forest Regression produced an MSE of 327.08 and an R² score of -0.0232. Similarly, for parents' income, Random Forest Regression resulted in an MSE of 327.08 and an R² score of -0.0232, and Neural Network Regression yielded an MSE of 323.78 and an R² score of -0.0129. These negative R² scores indicate that SES does not significantly impact physics exam scores, highlighting the complexity of factors influencing academic performance. This research suggests that other variables may play a more critical role in determining students' success in physics. This research underscores the need for a more comprehensive approach to understanding and supporting student achievement in education.
Keywords:
Academic performance Neural network Physics exam score Random forest regression Socio-economic statusReferences
Ahn, S. (2022). Building and analyzing machine learning-based warfarin dose prediction models using scikit-learn. Translational and Clinical Pharmacology, 30(4), 172. https://doi.org/10.12793/tcp.2022.30.e22
Antonoplis, S. (2023). Studying Socioeconomic Status: Conceptual Problems and an Alternative Path Forward. Perspectives on Psychological Science, 18(2), 275–292. https://doi.org/10.1177/17456916221093615
Belmi, P., Neale, M. A., Reiff, D., & Ulfe, R. (2020). The social advantage of miscalibrated individuals: The relationship between social class and overconfidence and its implications for class-based inequality. Journal of Personality and Social Psychology, 118(2), 254–282. https://doi.org/10.1037/pspi0000187
Camizuli, E., & Carranza, E. J. (2018). Exploratory Data Analysis ( EDA ). In The Encyclopedia of Archaeological Sciences (pp. 1–7). Wiley. https://doi.org/10.1002/9781119188230.saseas0271
Deta, U. A., Ayun, S. K., Laila, L., Prahani, B. K., Suprapto, N., & others. (2024). PISA science framework 2018 vs 2025 and its impact in physics education: Literature review. Momentum: Physics Education Journal, 8(1), 95–107. https://doi.org/10.21067/mpej.v8i1.9215
Doz, D., Cotič, M., & Felda, D. (2023). Random forest regression in predicting students’ achievements and fuzzy grades. Mathematics, 11(19), 4129. https://doi.org/10.3390/math11194129
Dubois, D., Rucker, D. D., & Galinsky, A. D. (2015). Social class, power, and selfishness: When and why upper and lower class individuals behave unethically. Journal of Personality and Social Psychology, 108(3), 436–449. https://doi.org/10.1037/pspi0000008
Duncan, G., & Menestrel, S. L. (Eds.). (2019). A Roadmap to Reducing Child Poverty. National Academies Press. https://doi.org/10.17226/25246
Erdem, C., & Kaya, M. (2023). Socioeconomic status and wellbeing as predictors of students’ academic achievement: evidence from a developing country. Journal of Psychologists and Counsellors in Schools, 33(2), 202–220. https://doi.org/10.1017/jgc.2021.10
Gong, H., Sun, Y., Shu, X., & Huang, B. (2018). Use of random forests regression for predicting IRI of asphalt pavements. Construction and Building Materials, 189, 890–897. https://doi.org/10.1016/j.conbuildmat.2018.09.017
Hilbert, S., Coors, S., Kraus, E., Bischl, B., Lindl, A., Frei, M., Wild, J., Krauss, S., Goretzko, D., & Stachl, C. (2021). Machine learning for the educational sciences. Review of Education, 9(3). https://doi.org/10.1002/rev3.3310
Hittner, E. F., Rim, K. L., & Haase, C. M. (2019). Socioeconomic status as a moderator of the link between reappraisal and anxiety: Laboratory-based and longitudinal evidence. Emotion, 19(8), 1478–1489. https://doi.org/10.1037/emo0000539
Krishnan, S., Reston, E., & Sukumaran, S. D. (2023). The Relationship between Malaysian Students’ Socio-Economic Status and their Academic Achievement in STEM education. International Journal of Learning, Teaching and Educational Research, 22(6), 533–551. https://doi.org/10.26803/ijlter.22.6.28
Kucak, D., Juricic, V., & Dambic, G. (2018). Machine Learning in Education - a Survey of Current Research Trends (pp. 0406–0410). https://doi.org/10.2507/29th.daaam.proceedings.059
Mastour, H., Dehghani, T., Moradi, E., & Eslami, S. (2023). Early prediction of medical students’ performance in high-stakes examinations using machine learning approaches. Heliyon, 9(7). Retrieved from https://www.cell.com/heliyon/fulltext/S2405-8440(23)05456-7?uuid=uuid%3A0df8d855-8cd4-4e5b-8a04-788cf7347b15
Nafea, I. T. (2018). Machine Learning in Educational Technology. In Machine Learning - Advanced Techniques and Emerging Applications. InTech. https://doi.org/10.5772/intechopen.72906
Rajamani, S. K., & Iyer, R. S. (2023). Machine learning-based mobile applications using Python and Scikit-Learn. In Designing and developing innovative mobile applications (pp. 282–306). IGI Global. Retrieved from https://www.igi-global.com/chapter/machine-learning-based-mobile-applications-using-python-and-scikit-learn/322076
Raza, S., Hameed, M., Abbas, N., Rizvi, S. W., & Sarfaraz, A. (2023). The dynamic interplay of socioeconomic factors in educational attainment: a holistic analysis of socioeconomic status and academic success. Int J Learn Divers Identities, 30(2), 204–213. Retrieved from https://shorturl.asia/mW1jz
Riyanto, A. D., Wahid, A. M., & Pratiwi, A. A. (2024). Analysis Of Factors Determining Student Satisfaction Using Decision Tree, Random Forest, Svm, And Neural Networks: A Comparative Study. Jurnal Teknik Informatika (Jutif), 5(4), 187–196. https://doi.org/10.52436/1.jutif.2024.5.4.2188
Rodríguez-Hernández, C. F., Cascallar, E., & Kyndt, E. (2020). Socio-economic status and academic performance in higher education: A systematic review. Educational Research Review, 29, 100305. https://doi.org/10.1016/j.edurev.2019.100305
Scabini, L. F. S., & Bruno, O. M. (2023). Structure and performance of fully connected neural networks: Emerging complex network properties. Physica A: Statistical Mechanics and Its Applications, 615, 128585. https://doi.org/10.1016/j.physa.2023.128585
Scardapane, S., & Wang, D. (2017). Randomness in neural networks: an overview. WIREs Data Mining and Knowledge Discovery, 7(2). https://doi.org/10.1002/widm.1200
Tran, M.-K., Panchal, S., Chauhan, V., Brahmbhatt, N., Mevawalla, A., Fraser, R., & Fowler, M. (2022). Python-based scikit-learn machine learning models for thermal and electrical performance prediction of high-capacity lithium-ion battery. International Journal of Energy Research, 46(2), 786–794. https://doi.org/10.1002/er.7202
Vadivel, B., Alam, S., Nikpoo, I., & Ajanil, B. (2023). The Impact of Low Socioeconomic Background on a Child’s Educational Achievements. Education Research International, 2023, 1–11. https://doi.org/10.1155/2023/6565088
Washbrook, E., Gregg, P., & Propper, C. (2014). A Decomposition Analysis of the Relationship Between Parental Income and Multiple Child Outcomes. Journal of the Royal Statistical Society Series A: Statistics in Society, 177(4), 757–782. https://doi.org/10.1111/rssa.12074
License
Copyright (c) 2025 Mutia Rosiana Nita Putri, Kertanah, Mugni Bustari, Muhammad Aminuddin, Baiq Husnul Khotimah
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with Jurnal Penelitian Pendidikan IPA, agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution 4.0 International License (CC-BY License). This license allows authors to use all articles, data sets, graphics, and appendices in data mining applications, search engines, web sites, blogs, and other platforms by providing an appropriate reference. The journal allows the author(s) to hold the copyright without restrictions and will retain publishing rights without restrictions.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in Jurnal Penelitian Pendidikan IPA.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
