Authors : Chinonso Job; Onwe, Festus Chijioke

Volume/Issue : Volume 11 - 2026, Issue 2 - February

Google Scholar : https://tinyurl.com/47mnmejn

Scribd : https://tinyurl.com/22vfsduv

DOI : https://doi.org/10.38124/ijisrt/26feb233

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

Abstract : The rapid expansion of online education necessitates rigorous empirical investigation into its effectiveness compared to traditional classroom instruction. This study presents a comprehensive statistical analysis examining the relationship between learning modality and academic outcomes, utilizing data from 500 student records obtained from the UCI Machine Learning Repository. Employing multiple linear regression and binary logistic regression methodologies, we evaluated how learning mode, attendance rates, study duration, and demographic variables influence academic performance and student satisfaction. Results indicated that students in traditional learning environments achieved significantly higher academic performance scores (M = 76) compared to online learners (M = 73), with attendance percentage emerging as a significant positive predictor (β = 0.10, 95% CI [0.05, 0.15], p < 0.001). Logistic regression analysis revealed that online students exhibited significantly lower satisfaction levels (OR = 0.65, 95% CI [0.45, 0.95], p < 0.05). The multiple regression model explained approximately 15% of variance in academic performance (R² = 0.15), suggesting the presence of unmeasured confounding variables. These findings carry important implications for educational policy and institutional decision-making regarding learning modality implementation. Recommendations include enhancing attendance monitoring systems for online courses, developing interactive digital learning tools, and implementing hybrid learning models that leverage the strengths of both modalities.

Keywords : Academic Performance, Student Satisfaction, Online Learning, Traditional Learning, Multiple Regression, Logistic Regression, Educational Technology.

References :

1. Allen, I. E., & Seaman, J. (2017). Digital compass learning: Distance education enrollment report 2017. Babson Survey Research Group.
2. Alqurashi, E. (2019). Predicting student satisfaction and perceived learning within online learning environments. Distance Education, 40(1), 133-148.
3. Bernard, R. M., Abrami, P. C., Lou, Y., Borokhovski, E., Wade, A., Wozney, L., Wallet, P. A., Fiset, M., & Huang, B. (2004). How does distance education compare with classroom instruction? A meta-analysis of the empirical literature. Review of Educational Research, 74(3), 379-439.
4. Credé, M., Roch, S. G., & Kieszczynka, U. M. (2010). Class attendance in college: A meta-analytic review of the relationship of class attendance with grades and student characteristics. Review of Educational Research, 80(2), 272-295.
5. Elliott, K. M., & Shin, D. (2002). Student satisfaction: An alternative approach to assessing this important concept. Journal of Higher Education Policy and Management, 24(2), 197-209.
6. Garrison, D. R., Anderson, T., & Archer, W. (2000). Critical inquiry in a text-based environment: Computer conferencing in higher education. The Internet and Higher Education, 2(2-3), 87-105.
7. Hodges, C., Moore, S., Lockee, B., Trust, T., & Bond, A. (2020). The difference between emergency remote teaching and online learning. EDUCAUSE Review, 27(1), 1-12.
8. Kuo, Y. C., Walker, A. E., Schroder, K. E., & Belland, B. R. (2014). Interaction, Internet self-efficacy, and self-regulated learning as predictors of student satisfaction in online education courses. The Internet and Higher Education, 20, 35-50.
9. Means, B., Toyama, Y., Murphy, R., & Baki, M. (2013). The effectiveness of online and blended learning: A meta-analysis of the empirical literature. Teachers College Record, 115(3), 1-47.
10. Muilenburg, L. Y., & Berge, Z. L. (2005). Student barriers to online learning: A factor analytic study. Distance Education, 26(1), 29-48.
11. Richardson, M., Abraham, C., & Bond, R. (2012). Psychological correlates of university students' academic performance: A systematic review and meta-analysis. Psychological Bulletin, 138(2), 353-387.
12. Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68-78.
13. Vo, H. M., Zhu, C., & Diep, N. A. (2017). The effect of blended learning on student performance at course-level in higher education: A meta-analysis. Studies in Educational Evaluation, 53, 17-28.
14. Zhao, Y., Lei, J., Yan, B., Lai, C., & Tan, H. S. (2005). What makes the difference? A practical analysis of research on the effectiveness of distance education. Teachers College Record, 107(8), 1836-1884.