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A Data-Driven Predictive Analysis of Academic Performance and Study Efficiency Among College Students


Authors : Romer M. Real; Jay T. Castor; Rolly B. Villanueva; Reginaldo Morijon; Cesar Joey Santillan; Serafin C. Palmares; Kristine T. Soberano

Volume/Issue : Volume 11 - 2026, Issue 5 - May


Google Scholar : https://tinyurl.com/3xzc4np8

Scribd : https://tinyurl.com/3e3e7y5u

DOI : https://doi.org/10.38124/ijisrt/26May654

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.


Abstract : This study examined the academic performance and study efficiency of college students, focusing on identifying critical behavioral predictors of success. The research was initially planned as a primary descriptive-survey using a 30-item Likert-scale questionnaire (Cronbach’s α = .85) for students in Negros Occidental. Due to ethical considerations during examination week, the study utilized a secondary data-driven dataset, a sample of 150 students was analyzed using a descriptive-predictive design with descriptive statistics and linear regression.

Keywords : Academic Performance; Study Habits; Time Management; Predictive Analytics; Study Efficiency.

References :

  1. Abdallah, N., & Abdullah, A. (2020). Predicting student performance using data mining and learning analytics techniques: A systematic literature review. Applied Sciences, 11(1), 237. https://doi.org/10.3390/app11010237.
  2. Al-Obaidi, H. (2023). Student grade prediction using machine learning methods. Rochester Institute of Technology. https://repository.rit.edu/cgi/viewcontent.cgi?article=13370&context=these.
  3. Ampomah, O., Agyekum, L., Akuoko-Frimpong, J., & Quansah, A. (2024). Predicting students' academic performance via machine learning algorithms: An empirical review and practical application. Computer Engineering and Intelligent Systems, 15(1). https://doi.org/10.7176/CEIS/15-1-09.
  4. Feng, J., Yu, B., Tan, W. H., Dai, Z., & Li, Z. (2025). Key factors influencing educational technology adoption in higher education: A systematic review. Retrieved from https://pmc.ncbi.nlm.nih.gov/articles/PMC12040101.
  5. Ji, M., Le, J., Chen, B., & Li, Z. (2024). A predictive model for classifying college students' academic performance based on visual-spatial skills. Frontiers in Psychology, 15, 1434015. https://doi.org/10.3389/fpsyg.2024.1434015.
  6. Lagman, A. (2015). Predictive decision support system using logistic regression and decision tree model combination for student graduation success determination. https://doi.org/10.21016/IRRC.2015.AU05EF82O.
  7. Luo, Z., Mai, J., Feng, C., Kong, D., Liu, J., Ding, J., Qi, B., & Zhu, Z. (2024). A method for prediction and analysis of student performance that combines multi-dimensional features of time and space. Mathematics, 12(22), 3597. https://doi.org/10.3390/math12223597.
  8. Weston, T. (2020). Efficiency in higher education: A quantitative analysis of academic effort and grade outcomes. Journal of Learning Analytics, 7(2), 45–58.
  9. Zuo, M., Wang, K., Tang, P., Meng, X., & Luo, H. (2025). Predicting academic performance from future-oriented daily time management behavior: A LASSO-based study of first-year college students. Behavioral Sciences, 15(9), 1242. https://doi.org/10.3390/bs15091242.

This study examined the academic performance and study efficiency of college students, focusing on identifying critical behavioral predictors of success. The research was initially planned as a primary descriptive-survey using a 30-item Likert-scale questionnaire (Cronbach’s α = .85) for students in Negros Occidental. Due to ethical considerations during examination week, the study utilized a secondary data-driven dataset, a sample of 150 students was analyzed using a descriptive-predictive design with descriptive statistics and linear regression.

Keywords : Academic Performance; Study Habits; Time Management; Predictive Analytics; Study Efficiency.

Paper Submission Last Date
31 - May - 2026

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