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Computers as Tutors: How Technology Can Help Bridge the Rural-Urban Education Gap in China


Credit: Peter Morgan

In much of the developed world, technology has rapidly brought itself into the forefront of education as services such as Google Classroom, Newsela, and Chegg have exploded in the past decade. Even so, many educational systems in the developing world lack the resources and funds required to implement and maintain an educational technology platform.

Nowhere is this disparity between developing and developed communities clearer than in China, where rural students perform significantly worse on standardized tests than urban students (Lai et al., 2012). Why? The per capita income in urban China is three times greater than the average per capita income in rural China (Statista), which leads to better-trained teachers, better facilities, and a greater investment per student (Lai et al., 2012). Additionally, urban students have readily accessible remedial tutoring after school, while rural schools struggle with long commute times for both teachers and students, a lack of commercial tutoring services due to population sparsity, and busy, poorly educated parents who are often unable to help their children (Lai et al., 2013). However, computer-assisted learning, or CAL, offers a prospective solution to these issues, as distance tutoring and computer programs can help compensate for substandard facilities and substitute for overworked teachers and parents.


How does Computer Assisted Learning impact student performance?

Early studies on CAL were based in developed nations, where educational resources are less scarce. As a result, their results were mixed, with most studies suggesting little to no improvement. However, more recent studies have analyzed the effectiveness of specific CAL implementations as well as delved into its impact on rural, underserved students.


The Shaanxi Study

Shaanxi Province is one of the poorest provinces in all of China with an average yearly salary of $600 per year in 2011, and thus provides the perfect environment to examine CAL’s effectiveness in rural, impoverished communities. A study in 2012 found that after-school CAL programs in Shaanxi Province demonstrably improved standardized math scores by 0.12 standard deviations, and even more so for poorer families. Overall, this suggests that after-school CAL programs are somewhat effective in underserved communities. However, CAL did not improve Chinese language standardized test scores, raising questions about whether CAL is effective for Chinese language learning outcomes and also if it can replace in-person instruction entirely (Lai et al., 2012).


The Qinghai Study

A recent study in Qinghai Province answers this question, finding that the method that CAL is implemented has a significant impact on its success. When comparing CAL implementations for an English program between an NGO, the local government, and a pure control, the government’s CAL system did not demonstrably improve student outcomes, while the NGO program improved students’ scores by 0.18 standard deviations (Mo et al., 2020). This difference can most directly be attributed to the government officials’ tendency to replace, rather than supplement, in-person instruction with CAL, leading to a decrease in instructional time. Additionally, NGOs were more likely to directly monitor the program’s progress, while government officials often failed to call or visit schools in the program. This may be because NGOs typically have more resources than the government in addition to being under greater pressure to get results. In essence, studies suggest that CAL is only effective as an after-school program in conjunction with in-person teaching, essentially providing remedial tutoring for children who otherwise could not afford or access it.


Where do we go from here?

These studies suggest that when implemented properly, CAL can have a positive impact on rural students’ learning outcomes. However, properly is the key word here. Further expansion of CAL needs to be heavily scrutinized by a private party, as a lack of check-ins, untrained teachers, technical difficulties, and governmental shortcuts can all hamper the positive effects of the program. Additionally, the Chinese government must invest in this expansion as well, as severe resource restrictions could easily place a chokehold on computer facilities, electricity, monitoring, and other critical infrastructure for CAL’s success. In essence, CAL represents a promising tool for educational reform, but it requires an extensive commitment from the local community, private monitoring agencies, and the Chinese government.


Sources:

Lai, Fang, et al. “Computer Assisted Learning as Extracurricular Tutor? Evidence from a Randomised Experiment in Rural Boarding Schools in Shaanxi.” Journal of Development Effectiveness, vol. 5, no. 2, 2013, pp. 208–231., doi:10.1080/19439342.2013.780089.


Lai, F., Liu, C., Luo, R., Zhang, L., Ma, X., Bai, Y., Sharbono, B and Rozelle, S. 2012. “Private Migrant Schools or Rural/Urban Public Schools: Where Should China Educate Its Migrant Children?”


Mo, Di, et al. “Institutions, Implementation, and Program Effectiveness: Evidence from a Randomized Evaluation of Computer-Assisted Learning in Rural China.” Journal of Development Economics, vol. 146, 2020, p. 102487., doi:10.1016/j.jdeveco.2020.102487.


“China: Per Capita Disposable Income of Rural and Urban Households 2018.” Statista, https://www.statista.com/statistics/259451/annual-per-capita-disposable-income-of-rural-and-urban-households-in-china/. Accessed 22 June 2020.


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