
A Multi-Dimensional Framework for Comparing Digital Learning Effectiveness Between Rural and Urban Higher Education Students | IJCT Volume 13 – Issue 6 | IJCT-V13I6P83
IJCT
International Journal of Computer Techniques
ISSN 2394-2231 · Peer-Reviewed · Open Access
📚 Volume 13, Issue 6
📅 November 11, 2026
📄 Pages 710–720
🔖 ID: IJCT-V13I6P83
Table of Contents
ToggleA Multi-Dimensional Framework for Comparing Digital Learning Effectiveness Between Rural and Urban Higher Education Students
Author(s)
Pranaket Kamble, Omkar Nalawade, Anurag Mishra, Suraj Talape, Dr. Prashant Chordiya
Abstract
Digital learning is now routine in Indian higher education, yet the evidence on whether rural and urban students benefit equally is mixed. We present DLEF, a measurement-and-decomposition framework with an open reference implementation. It compares rural and urban higher-education students in a single sample on performance impact, engagement, satisfaction, digital literacy, self-efficacy and infrastructure. DLEF scores six instrument dimensions, forms a standardised composite outcome index, tests group differences with Welch and Mann-Whitney tests, Hedges’ g, bootstrap intervals and Holm correction, splits the urban-rural gap into explained and unexplained parts with a Blinder-Oaxaca decomposition, and tests a literacy, self-efficacy, engagement pathway by bootstrap mediation. Fifteen automated tests check that the implementation recovers effects planted in synthetic data and reports none where none were planted. Data collection is planned for Pune district, Maharashtra. This manuscript reports no empirical results about students. The results section lists the outputs we will report once the collected data are analysed. We draw no conclusion about rural-urban differences here.
Keywords
digital divide, digital learning effectiveness, digital literacy, rural-urban disparity, higher education, Blinder-Oaxaca decomposition, educational technology
Conclusion
We specified DLEF, a framework for comparing digital learning effectiveness between rural and urban higher-education students on six dimensions in one sample, and gave a protocol, a mathematical formulation and a tested reference implementation. The framework combines established statistical methods and proposes the composite DLE index and a specific application of the Blinder-Oaxaca and mediation analyses to this question. The paper reports no empirical findings. Whether rural students experience lower digital learning effectiveness than urban students, and how much of any difference is associated with infrastructure, literacy and self-efficacy, remains for the data to say.
References
REFERENCES
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[3]B. T. Sampath Kumar et al., "The digital divide in India: use and non-use of ICT by rural and urban students," World J. Science, Technology and Sustainable Development, 2018.
[4]A. R. Sathyan et al., "Digital competence of higher education learners in the context of COVID-19 triggered online learning," Social Sciences & Humanities Open, 2022.
[5]L. Zhao et al., "Determinants of the digital outcome divide in e-learning between rural and urban students: Empirical evidence from the COVID-19 pandemic based on capital theory," Computers in Human Behavior, 2021.
[6]M. Raza et al., "A Comparative Study of Digital Divide and Learning Outcomes between Urban and Rural Undergraduates," Social Prism, 2026.
[7]H.-Y. Wang et al., "Revisiting the Digital Divide in Online Teaching: An Analysis of Urban-Rural Differences in Online Learning among Middle School Students," Open Journal of Social Sciences, 2022.
[8]T. Kavitha, "Digital Learning Preparedness and Technology Engagement among Higher Education Students: An Empirical Study," Int. J. Informative and Futuristic Research, 2026.
[9]K. Dhanasree et al., "Exploring Digital Competencies and Online Learning Readiness among Undergraduate Students," Advances in Research, 2025.
[10]F. Bast, "Perception of Online Learning Among Students From India Set Against the Pandemic," 2021.
[11]G. Tiwari et al., "Digital Divide and Its Impact on English Language Learning Among Rural vs. Urban ESL Learners in India," Int. J. Humanities, Commerce and Education, 2026.
[12]S. Getenet et al., "Students’ digital technology attitude, literacy and self-efficacy and their effect on online learning engagement," Int. J. Educational Technology in Higher Education, 2024.
[13]Y. Feng et al., "Digital literacy is associated with college students’ learning engagement via sequential mediation of self-efficacy and learning adaptability," Scientific Reports, 2026.
[14]S. Bandyopadhyay et al., "Exploring Rural-Urban Education Divide in India," book chapter, 2021.
[15]A. Mathrani et al., "Rural-Urban, Gender, and Digital Divides during the COVID-19 Lockdown: A Multi-Layered Study," Societies, vol. 13, no. 5, art. 122, 2023.
[16]L. J. Cronbach, "Coefficient alpha and the internal structure of tests," Psychometrika, vol. 16, no. 3, pp. 297-334, 1951.
[17]H. B. Mann and D. R. Whitney, "On a test of whether one of two random variables is stochastically larger than the other," Ann. Math. Statist., vol. 18, no. 1, pp. 50-60, 1947.
[18]L. V. Hedges, "Distribution theory for Glass’s estimator of effect size and related estimators," J. Educ. Statist., vol. 6, no. 2, pp. 107-128, 1981.
[19]B. Efron, "Bootstrap methods: Another look at the jackknife," Ann. Statist., vol. 7, no. 1, pp. 1-26, 1979.
[20]S. Holm, "A simple sequentially rejective multiple test procedure," Scand. J. Statist., vol. 6, no. 2, pp. 65-70, 1979.
[21]R. L. Oaxaca, "Male-female wage differentials in urban labor markets," Int. Econ. Rev., vol. 14, no. 3, pp. 693-709, 1973.
[22]A. S. Blinder, "Wage discrimination: Reduced form and structural estimates," J. Human Resources, vol. 8, no. 4, pp. 436-455, 1973.
[23]D. Neumark, "Employers’ discriminatory behavior and the estimation of wage discrimination," J. Human Resources, vol. 23, no. 3, pp. 279-295, 1988.
[24]K. J. Preacher and A. F. Hayes, "Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models," Behav. Res. Methods, vol. 40, no. 3, pp. 879-891, 2008.
[1]T. Muthuprasad et al., "Students’ perception and preference for online education in India during COVID-19 pandemic," Social Sciences & Humanities Open, vol. 3, no. 1, 2021.
[2]A. Lokhande et al., "Digital Education in India: Bridging the Divide or Widening the Gap?," Int. J. Research in Applied Science and Engineering Technology, 2026.
[3]B. T. Sampath Kumar et al., "The digital divide in India: use and non-use of ICT by rural and urban students," World J. Science, Technology and Sustainable Development, 2018.
[4]A. R. Sathyan et al., "Digital competence of higher education learners in the context of COVID-19 triggered online learning," Social Sciences & Humanities Open, 2022.
[5]L. Zhao et al., "Determinants of the digital outcome divide in e-learning between rural and urban students: Empirical evidence from the COVID-19 pandemic based on capital theory," Computers in Human Behavior, 2021.
[6]M. Raza et al., "A Comparative Study of Digital Divide and Learning Outcomes between Urban and Rural Undergraduates," Social Prism, 2026.
[7]H.-Y. Wang et al., "Revisiting the Digital Divide in Online Teaching: An Analysis of Urban-Rural Differences in Online Learning among Middle School Students," Open Journal of Social Sciences, 2022.
[8]T. Kavitha, "Digital Learning Preparedness and Technology Engagement among Higher Education Students: An Empirical Study," Int. J. Informative and Futuristic Research, 2026.
[9]K. Dhanasree et al., "Exploring Digital Competencies and Online Learning Readiness among Undergraduate Students," Advances in Research, 2025.
[10]F. Bast, "Perception of Online Learning Among Students From India Set Against the Pandemic," 2021.
[11]G. Tiwari et al., "Digital Divide and Its Impact on English Language Learning Among Rural vs. Urban ESL Learners in India," Int. J. Humanities, Commerce and Education, 2026.
[12]S. Getenet et al., "Students’ digital technology attitude, literacy and self-efficacy and their effect on online learning engagement," Int. J. Educational Technology in Higher Education, 2024.
[13]Y. Feng et al., "Digital literacy is associated with college students’ learning engagement via sequential mediation of self-efficacy and learning adaptability," Scientific Reports, 2026.
[14]S. Bandyopadhyay et al., "Exploring Rural-Urban Education Divide in India," book chapter, 2021.
[15]A. Mathrani et al., "Rural-Urban, Gender, and Digital Divides during the COVID-19 Lockdown: A Multi-Layered Study," Societies, vol. 13, no. 5, art. 122, 2023.
[16]L. J. Cronbach, "Coefficient alpha and the internal structure of tests," Psychometrika, vol. 16, no. 3, pp. 297-334, 1951.
[17]H. B. Mann and D. R. Whitney, "On a test of whether one of two random variables is stochastically larger than the other," Ann. Math. Statist., vol. 18, no. 1, pp. 50-60, 1947.
[18]L. V. Hedges, "Distribution theory for Glass’s estimator of effect size and related estimators," J. Educ. Statist., vol. 6, no. 2, pp. 107-128, 1981.
[19]B. Efron, "Bootstrap methods: Another look at the jackknife," Ann. Statist., vol. 7, no. 1, pp. 1-26, 1979.
[20]S. Holm, "A simple sequentially rejective multiple test procedure," Scand. J. Statist., vol. 6, no. 2, pp. 65-70, 1979.
[21]R. L. Oaxaca, "Male-female wage differentials in urban labor markets," Int. Econ. Rev., vol. 14, no. 3, pp. 693-709, 1973.
[22]A. S. Blinder, "Wage discrimination: Reduced form and structural estimates," J. Human Resources, vol. 8, no. 4, pp. 436-455, 1973.
[23]D. Neumark, "Employers’ discriminatory behavior and the estimation of wage discrimination," J. Human Resources, vol. 23, no. 3, pp. 279-295, 1988.
[24]K. J. Preacher and A. F. Hayes, "Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models," Behav. Res. Methods, vol. 40, no. 3, pp. 879-891, 2008.
📋 How to Cite This Paper
Pranaket Kamble, Omkar Nalawade, Anurag Mishra, Suraj Talape, Dr. Prashant Chordiya (2026). A Multi-Dimensional Framework for Comparing Digital Learning Effectiveness Between Rural and Urban Higher Education Students. International Journal of Computer Techniques, 13(6), 710–720. ISSN: 2394-2231. DOI: https://doi.org/10.5281/zenodo.24131671










