Abstract
This paper aims to investigate whether online private supplementary education, also known as shadow education, can alleviate educational inequality and what types of mechanisms can help alleviate it. We investigate this using an online learning platform dataset (3,603 anonymous students from China) with additional data from multiple sources and employ geospatial analyses to measure students’ socioeconomic, regional and rural/urban inequalities. We find that taking part in online education narrows the performance gap in mathematics between privileged and unprivileged students in terms of school status and regional disparity in China. Theoretically, two micro-level mechanisms explain the alleviating differences: 1) equal access mechanism: students from lower city tiers and low-status schools show greater score improvement when having equal access to online education; 2) equal quality mechanism: students from rural regions improve their in-class rankings more substantially if they receive equal quality online education with the same tutoring and learning environment alongside urban students. This study comprehensively looks at the different effects of two mechanisms of online education—equal access and equal quality—for alleviating various types of inequality. Thus we speak to both educational inequality and digital inequality theory, finding that equal access to online education is not enough for rural students, as they also need access to classes of equal educational quality with their urban counterparts.
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The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
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iReasearch Center. The Research Report on China's Online Education Industry in 2020. https://www.iresearch.com.cn/Detail/report?id=3724&isfree=0.
References
Ahmad, N. A., Ayub, A. F. M., & Khambari, M. N. (2019). Gender digital divide: digital skills among Malaysian secondary school. International Journal of Academic Research in Progressive Education and Development, 8(4), 668–687. https://doi.org/10.18410/jebmh/2019/587
Aissaoui, N. (2021). The digital divide: a literature review and some directions for future research in light of COVID-19. Global Knowledge, Memory and Communication, ahead-of-print https://doi.org/10.1108/GKMC-06-2020-0075
Ateeq, K., Mago, B., & Pradhan, M. R. (2021). A novel flexible data analytics model for leveraging the efficiency of smart education. Soft Computing, 25(18), 12305–12318. https://doi.org/10.1007/s00500-021-05925-9
Author (2021).
Baig, M. I., Shuib, L., & Yadegaridehkordi, E. (2020). Big data in education: A state of the art, limitations, and future research directions. International Journal of Educational Technology in Higher Education., 17, 44. https://doi.org/10.1186/s41239-020-00223-0
Bandura, A. (1977). Social Learning Theory. Prentice-Hall.
Boliver, V. (2013). How fair is access to more prestigious UK universities. The British Journal of Sociology, 64(2), 344–364. https://doi.org/10.1111/1468-4446.12021
Bray, M. (1999). The shadow education system: Private tutoring and its implications for planners. UNESCO.
Bray, M. (2006). Private supplementary tutoring: Comparative perspectives on patterns and implications. Compare, 36(4), 515–530.
Bray, M. (2013). Shadow education: Comparative perspectives on the expansion and implications of private supplementary tutoring. Procedia - Social and Behavioral Sciences, 77, 412–420.
Bray, M. (2021). Geographies of shadow education: Patterns and forces in the spatial distributions of private supplementary tutoring. Compare: A Journal of Comparative and International Educationhttps://doi.org/10.1080/03057925.2021.1915749
Breda, T., Jouini, E., & Napp, C. (2018). Societal inequalities amplify gender gaps in math. Science, 359(6381), 1219–1220. https://doi.org/10.1126/science.aar2307
Caballero, C., Haynes, J., & Tikly, L. (2007). Researching mixed race in education: Perceptions, policies and practices. Race Ethnicity and Education, 10(3), 345–362. https://doi.org/10.1080/13613320701503389
Caro, D. H., & Biecek, P. (2017). intsvy: An R package for analyzing international large-scale assessment data. Journal of Statistical Software, 81(7), 1–44. https://doi.org/10.18637/jss.v081.i07
Castells, M. (2002). The Internet Galaxy: Reflections on the Internet, Business and the Society. Oxford University Press.
China Internet Network Information Center. (2020). The 46th statistical report on the development of China's Internet. https://www.cnnic.com.cn/IDR/ReportDownloads/202012/P020201201530023411644.pdf. Accessed 22 Sep 2023.
Chirikov, I., Semenova, T., Maloshonok, N., Bettinger, E., & Kizilcec, R. F. (2020). Online education platforms scale college STEM instruction with equivalent learning outcomes at lower cost. Science Advance, 6(15), aay5324. https://doi.org/10.1126/sciadv.aay5324
Dang, H. A., & Rogers, F. H. (2008). The growing phenomenon of private tutoring: Does it deepen human capital, widen inequalities, or waste resources? The World Bank Research Observer, 23(2), 161–200. https://doi.org/10.1093/wbro/lkn004
Daoud, R., Starkey, L., Eppel, E., Vo, T., & Sylvester, A. (2020). The educational value of internet use in the home for school children: A systematic review of literature. Journal of Research on Technology in Education, 53(4), 353–374. https://doi.org/10.1080/15391523.2020.1783402
Ding, S., Gao, X., Dong, Y., Tong, Y., & Fu, X. (2021). Estimating Multiple Socioeconomic Attributes via Home Location—A Case Study in China, The Journal of Social Computing, 2(1), 71–88.
Emery, N., Maher, J. M., & Ebert-May, D. (2021). Environmental influences and individual characteristics that affect learner-centered teaching practices. PloS One, 16(4), e0250760. https://doi.org/10.1371/journal.pone.0250760
Evans, J. (2020). Social computing unhinged. Journal of Social Computing, 1, 1–13. https://doi.org/10.23919/jsc.2020.0002
Friedman, J. (2020). Measuring and forecasting progress towards the education-related SDG targets. Nature, 580, 636–639. https://doi.org/10.1038/s41586-020-2198-8
González-Betancor, S. M., López-Puig, A. J., & Cardenal, M. E. (2021). Digital inequality at home. The school as compensatory agent. Computers & Education, 168(6), 104195. https://doi.org/10.1016/j.compedu.2021.104195
Graetz, N., Friedman, J., Osgood-Zimmerman, A., et al. (2018). Mapping local variation in educational attainment across Africa. Nature, 555(7694), 48–53. https://doi.org/10.1038/nature25761
Gube, J., & Gao, F. (2019). Education, Ethnicity and Equity in the Multilingual Asian Context. Springer.
Guo, Y., Chen, Q., Zhai, S., & Pei, C. (2020). Does private tutoring improve student learning in China? evidence from the China education panel survey. Asia & the Pacific Policy Studies, 7, 322–343. https://doi.org/10.1002/app5.310
Hargittai, E. (2002). The second-level digital divide: Differences in people’s online skills. First Monday, 7(4), 1–20. https://doi.org/10.5210/fm.v7i4.942
Hu, Y., Wen, F., & Ding, W. (2013). Does shadow education enlarge educational inequality? An empirical study based on the PISA 2012 Shanghai data(in Chinese). Peking University Education Review, 13(3), 29–46+188.
Hu, Y., Wen, F., & Ding, W. (2017). Does “Shadow Education” Enlarge the Inequality of Educational Outcomes? (in Chinese). China Economics of Education Review, 2(5), 43–71.
Kuan, P.-Y. (2019). Effects of Cram Schooling on Academic Achievement and Mental Health of Junior High Students in Taiwan. Chinese Sociological Review, 50(11), 1–30. https://doi.org/10.1080/21620555.2018.1526069
Li, Y., & Ranieri, M. (2013) Educational and social correlates of the digital inequality for rural and urban children: A study on primary school students in a provincial city of China. Computers & Education, 60(1), 197–209. https://www.learntechlib.org/p/132171/
List, J. A., Pernaudet, J., & Suskind, D. L. (2021). Shifting parental beliefs about child development to foster parental investments and improve school readiness outcomes. Nature Communication, 12, 5765. https://doi.org/10.1038/s41467-021-25964-y
Liu, Y. C., Lin, C. Y., Chen, H. H., & Huang, H. J. (2017). The effects of cram schooling on the ethnic learning achievement gap: Evidence from elementary school students in Taiwan. International Journal of Learning, Teaching and Educational Research, 16(1), 84–102.
Local Burden of Disease Educational Attainment Collaborators. (2020). Mapping disparities in education across low- and middle-income countries. Nature, 577, 235–238. https://doi.org/10.1038/s41586-019-1872-1
Luo, J., & Chan, C. (2022). Influences of shadow education on the ecology of education – A review of the literature. Educational Research Review, 36, 100450. https://doi.org/10.1016/j.edurev.2022.100450
Mishra, S., Smoreda, Z., & Fiore, M. (2022). Second-level digital divide: A longitudinal study of mobile traffic consumption imbalance in France. Proceedings of the ACM Web Conference 2022 (WWW '22) (pp. 2532–2540). New York, USA: Association for Computing Machinery.
OECD. (2017). PISA 2015 Mathematics Framework. In PISA 2015 Assessment and analytical framework: Science, reading, mathematic, financial literacy and collaborative problem solving. OECD. https://www.oecd-ilibrary.org/docserver/9789264281820-5-en.pdf?expires=1695356881&id=id&accname=guest&checksum=207E03DAB91F60AB2DF5D169FD8674A6. Accessed 22 Sep 2023.
Quarles, C. L., Budak, C., & Resnick, P. (2020). The shape of educational inequality. Science Advances, 6(29), aaz5954. https://doi.org/10.1126/sciadv.aaz5954
Wilcox, R. R. (2022). 10.9.1 MM-Estimator. In Introduction to Robust Estimation and Hypothesis Testing (5th ed.). Elsevier. https://www.sciencedirect.com/topics/mathematics/mm-estimator. Accessed 22 Sep 2023.
Ren, W., Zhu, X., & Yang, J. (2022). The SES-based difference of adolescents’ digital skills and usages: An explanation from family cultural capital. Computers & Education, 177(7), 104382. https://doi.org/10.1016/j.compedu.2021.104382
Ross, C. E., & Mirowsky, J. (1989). Explaining the social patterns of depression: Control and problem solving-or support and talking. Journal of Health Social Behavior, 30(2), 206–219. https://doi.org/10.2307/2137014
Ross, C. E., & Mirowsky, J. (2010). Gender and the health benefits of education. The Sociological Quarterly, 51(1), 1–19. https://doi.org/10.1111/j.1533-8525.2009.01164.x
Salganik, M. J. (2017). Bit by bit: Social research in the digital age. Princeton University Press.
Scheerder, A., van Deursen, A., & van Dijk, J. A. G. M. (2017). Determinants of Internet skills, uses and outcomes. A systematic review of the second- and third-level digital divide. Telematics and Informatics, 34(8), 1607–1624. https://doi.org/10.1016/j.tele.2017.07.007
Selwyn, N. (2004). Reconsidering political and popular understandings of the digital divide. New Media & Society, 6(3), 341–362. https://doi.org/10.1177/1461444804042519
Shank, D. B., & Cotten, S. R. (2014). Does technology empower urban youth? The relationship of technology use to self-efficacy. Computers & Education, 70(5), 184–193. https://doi.org/10.1016/j.compedu.2013.08.018
Shidaganti, G., & Prakash, S. (2021). A Comprehensive Framework for Big Data Analytics in Education. International Journal of Advanced Computer Science and Applications, 12(9), 218-227. 12. https://doi.org/10.14569/IJACSA.2021.0120926
Song, K. O., Park, H. J., & Sang, K. A. (2013). A cross-national analysis of the student- and school-level factors affecting the demand for private tutoring. Asia Pacific Education Review, 14(2), 125–139. https://doi.org/10.1007/s12564-012-9236-7
Sosa Díaz, M. J. (2021). Emergency Remote Education, Family Support and the Digital Divide in the Context of the COVID-19 Lockdown. International Journal of Environmental Research and Public Health, 18(15), 7956. https://doi.org/10.3390/ijerph18157956
Stevenson, D. L., & Baker, D. P. (1992). Shadow Education and Allocation in Formal Schooling: Transition to University in Japan. American Journal of Sociology, 97(6), 1639–1657.
Tsang, M. C. (2002). Comparing the costs of public and private schools in developing countries. In H. Levin & P. McEwan (Eds.), Yearbook of the American Education Finance Association. Eye on Education.
UN. (2015). Transforming our world: The 2030 agenda for sustainable development. Retrieved September 25, 2015, from https://sustainabledevelopment.un.org/content/documents/21252030%20Agenda%20for%20Sustainable%20Development%20web.pdf
UNESCO. (2017). Accountability in education: meeting our commitments. In Global education monitoring report. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000259338. Accessed 22 Sep 2023.
van Dijk, J. A. G. M. (2006). Digital divide research, achievements and shortcomings. Poetics, 34(s4–5), 221–235. https://doi.org/10.1016/j.poetic.2006.05.004
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
Wang, J., & Wu, Y. (2021). Private supplementary education and Chinese adolescents’ development: The moderating effects of family socioeconomic status. Journal of community psychology, 1–23. https://doi.org/10.1002/jcop.22774
Wei, Y. D., Xiao, W., Simon, C. A., Liu, B., & Ni, Y. (2018). Neighborhood, race and educational inequality. Cities, 73, 1–13. https://doi.org/10.1016/j.cities.2017.09.013
Xue, H., & Ding, X. (2009). Research on educational remediation of urban students in China. Educational Research, 1, 39–46.
Zhang, M. (2015). Internet use that reproduces educational inequalities: Evidence from big data. Computers & Education, 86, 212–223. https://doi.org/10.1016/j.compedu.2015.08.007
Zhao, L., Lu, Y., Huang, W., & Wang, Q. (2010). Internet inequality: The relationship between high school students’ Internet use in different locations and their Internet self-efficacy. Computers & Education, 55(4), 1405–1423. https://doi.org/10.1016/j.compedu.2010.05.010
Zhao, T., Huang, H., Yao, X., Luo, J.-D., & Fu, X. (2020). Predicting Individual Socioeconomic Status from Mobile Phone Data: A Semi-supervised Hypergraph-based Factor Graph Approach, International Journal of Data Science and Analytics, 9, 361–372.
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Gao, X., Luo, J., Chen, H. et al. Alleviating educational inequality in math with the aid of online shadow education– the impact of equal access and equal quality mechanisms. Educ Inf Technol 29, 10571–10593 (2024). https://doi.org/10.1007/s10639-023-12214-5
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DOI: https://doi.org/10.1007/s10639-023-12214-5