Student Performance Classification Using Academic, Socioeconomic, and Digital Behavior Features: A Comparative Study
DOI:
https://doi.org/10.63158/journalisi.v8i1.1460Keywords:
Performance prediction, Machine learning, LMS analytics, Digital behavior, Indonesian higher educationAbstract
Accurate prediction of student academic performance is essential for universities seeking to improve learning outcomes and deliver timely, data-driven support. Prior work commonly uses regression to estimate Grade Point Average (GPA), yet numeric predictions can be difficult for administrators to translate into actionable risk levels. This study reframes the task as binary classification, categorizing students as good (GPA ≥ 3.00) or poor (GPA < 3.00) performers. Using 2,423 records from multiple programs at an Indonesian university, we combine academic indicators from the learning management system (login frequency, assignment submission, and forum activity) with socio-economic and digital behavioral variables (parental income, extracurricular participation, study-group involvement, and social media use). Seven machine learning models—Naïve Bayes, Generalized Linear Model, Logistic Regression, Deep Learning, Decision Tree, Random Forest, and Gradient Boosted Trees (GBT)—are benchmarked under a consistent evaluation design. Results indicate that integrating academic, socio-economic, and digital behavioral features improves classification performance, and ensemble methods outperform single, traditional models. GBT yields the best accuracy of 0.75, offering a practical basis for early-warning dashboards and targeted interventions. The study provides comparative evidence from Indonesian higher education and highlights the value of incorporating digital engagement signals alongside conventional academic data for more effective student support services.
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References
[1] A. Alshanqiti and A. Namoun, “Predicting Student Performance and Its Influential Factors Using Hybrid Regression and Multi-Label Classification,” vol. 8, pp. 203827–203844, 2020, doi: 10.1109/ACCESS.2020.3036572.
[2] S. M. Dol and P. M. Jawandhiya, “Systematic Review and Analysis of EDM for Predicting the Academic Performance of Students,” J. Inst. Eng. Ser. B, vol. 105, no. 4, pp. 1021–1071, 2024, doi: 10.1007/s40031-024-00998-0.
[3] Khan, A., and S. K. Ghosh, “Student performance analysis and prediction in classroom learning: A review of educational data mining studies,” Educ. Inf. Technol., vol. 26, no. 1, pp. 205–240, 2021.
[4] I. Papadogiannis and M. Wallace, “Educational Data Mining : A Foundational Overview,” Encyclopedia, vol. 4, no. 4, pp. 1644–1664, 2024.
[5] P. E. O. David Iyanuoluwa Ajiga, Oladimeji Hamza, Adeoluwa Eweje, Eseoghene Kokogho, “Data-Driven Strategies for Enhancing Student Success in,” vol. 11, no. 1, pp. 411–424, 2025, doi: 10.56201/ijssmr.vol.11no1.2025.pg.411.424.
[6] N. P.-M. Esomonu, “Utilizing AI and Big Data for Predictive Insights on Institutional Performance and Student Success: A Data-Driven Approach to Quality Assurance,” AI Ethics, Acad. Integr. Futur. Qual. Assur. High. Educ., vol. 29, 2025.
[7] Gonugunta, K. C., and K. Leo, “Role of data-driven decision making in enhancing higher education performance: A comprehensive analysis of analytics in institutional management,” Int. J. Acta Informat., vol. 3, no. 1, pp. 149–159, 2024.
[8] Wakeel, S., D. Sher, A. Kauser, and K. B. A. H. K. Niazi, “Investigating how predictive analytics and student data modeling influence interventions, curriculum design, and educational policy,” Crit. Rev. Soc. Sci. Stud., vol. 3, no. 2, pp. 1506–1521, 2025.
[9] H. Luo, “Prediction of Student Decision-Making Behaviour based on Machine Learning Algorithms,” Pakistan J. Life Soc. Sci., vol. 22, no. 2, pp. 16382–16390, 2024.
[10] A. Mahmoud et al., “Policy Reviews in Higher Education Understanding the drivers of student loan decision-making and its impact on graduation rates in Ghanaian public universities,” Policy Rev. High. Educ., vol. 8, no. 1, pp. 85–101, 2024, doi: 10.1080/23322969.2024.2358008.
[11] Thelma, C. C., “Student retention in higher learning institutions of Zambia,” Int. J. Res. Publ. Rev., vol. 5, no. 6, pp. 433–441, 2024.
[12] Gul, M. N., W. Abbasi, M. Z. Babar, A. Aljohani, and M. Arif, “Data driven decisions in education using a comprehensive machine learning framework for student performance prediction,” Discover Comput., vol. 28, no. 1, Art. no. 153, 2025.
[13] K. Patil, K. Yesugade, and K. B. Naikwadi, “A Study on Regression Based Machine Learning Models to Predict the Student Performance,” J. Eng. Educ. Transform., pp. 177–186, 2024, doi: 10.16920/jeet/2024/v38i2/24200.
[14] H. Almaghrabi, B. Soh, and A. Li, “SoK : The Impact of Educational Data Mining on Organisational Administration,” Information, vol. 15, no. 11, p. 738, 2024.
[15] H. Sahlaoui, E. L. Arbi, A. Alaoui, and M. M. Jaber, “Predicting and Interpreting Student Performance Using Ensemble Models and Shapley Additive Explanations,” IEEE Access, vol. 9, pp. 152688–152703, 2021, doi: 10.1109/ACCESS.2021.3124270.
[16] P. Boozary, S. Sheykhan, H. Ghorbantanhaei, and C. Magazzino, “International Journal of Information Enhancing customer retention with machine learning : A comparative analysis of ensemble models for accurate churn prediction,” Int. J. Inf. Manag. Data Insights, vol. 5, no. 1, p. 100331, 2025, doi: 10.1016/j.jjimei.2025.100331.
[17] D. J. Lemay, C. Baek, and T. Doleck, “Computers and Education : Arti fi cial Intelligence Comparison of learning analytics and educational data mining : A topic modeling approach,” Comput. Educ. Artif. Intell., vol. 2, no. March, p. 100016, 2021, doi: 10.1016/j.caeai.2021.100016.
[18] M. Motevalli, “Comparative analysis of systematic, scoping, umbrella, and narrative reviews in clinical research: critical considerations and future directions,” Int. J. Clin. Pract., vol. 2025, no. 1, Art. no. 9929300, 2025.
[19] R. Awashreh, “Bridging Cultural Gaps : Enhancing Student Motivation and Academic Integrity in Oman ’ s Universities,” Forum Linguist. Stud., vol. 07, no. 02, pp. 265–279, 2025.
[20] P. Banerjee, “Connecting the dots: A systematic review of explanatory factors linking contextual indicators, institutional culture and degree awarding gaps,” High. Educ. Eval. Dev., vol. 18, no. 1, pp. 31–52, 2024, doi: 10.1108/HEED-07-2023-0020.
[21] T. Getaneh, T. Zaw, and K. Jozsa, “Bridging theoretical gaps to improve students’ academic success in higher education in the digital era: A systematic literature review,” Int. J. Educ. Res. Open, vol. 9, no. April, p. 100510, 2025, doi: 10.1016/j.ijedro.2025.100510.
[22] E. N. Syamiya, S. Lestari, D. Wulandari, and D. Ekawati, “Digital literacy analysis on online learning outcomes for macroeconomics with gender-mediated and family socio-economics as moderating variables,” J. Kependidikan, vol. 8, no. 2, pp. 450–459, 2022.
[23] B. Maunah, “Social and cultural capital and learners’ cognitive ability: Issues and prospects for educational relevance, access and equity towards digital communication in Indonesia,” J. Soc. Stud. Educ. Res., vol. 11, no. 1, pp. 163–191, 2020.
[24] B. A. Al-sheeb, A. M. Hamouda, and G. M. Abdella, “Modeling of student academic achievement in engineering education using cognitive and non-cognitive factors,” vol. 11, no. 2, pp. 178–198, 2025, doi: 10.1108/JARHE-10-2017-0120.
[25] M. Mohzana, “The impact of the new student orientation program on the adaptation process and academic performance,” Int. J. Educ. Narrat., vol. 2, no. 2, pp. 169–178, 2024.
[26] G. J. Palardy, “School peer non-academic skills and academic performance in high school,” Front. Educ., vol. 4, p. 57, 2019, doi: 10.3389/feduc.2019.00057.
[27] M. Vaarma and H. Li, “Technology in Society Predicting student dropouts with machine learning : An empirical study in Finnish higher education,” Technol. Soc., vol. 76, no. February, p. 102474, 2024, doi: 10.1016/j.techsoc.2024.102474.
[28] S. Zhao, D. Zhou, H. Wang, and D. Chen, “Enhancing Student Academic Success Prediction Through Ensemble Learning and Image-Based Behavioral Data Transformation,” Appl. Sci., vol. 15, no. 3, p. 1231, 2025.
[29] T. K. Shoukath and M. Chakkaravarthy, “Predictive analytics in education: machine learning approaches and performance metrics for student success—a systematic literature review,” Data Metadata, vol. 4, Art. no. 730, 2025, doi: 10.56294/dm2025730.
[30] M. J. Shayegan and R. Akhtari, “A Stacking Machine Learning Model for Student Performance Prediction Based on Class Activities in E-Learning,” Comput. Syst. Sci. Eng., vol. 48, no. 5, 2024, doi: 10.32604/csse.2024.052587.
[31] T. T. Id and Z. Li, “Predicting learning achievement using ensemble learning with result explanation,” PLoS One, vol. 20, no. 1, p. e0312124, 2025, doi: 10.1371/journal.pone.0312124.
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