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An Approach to Observing China’s Higher Educational Equity: The Application of Multimedia and Database in Liberal Arts Education After Lockdown

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The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIOT 2020)

Abstract

By studying the use of Multimedia and Database in Chinese liberal arts education, this paper argues that science and technology have a significant impact on the fairness of liberal arts education in China apart from those universities in developed regions. Through analyzing the use of two academic tools provided by universities in different regions, it can be concluded that in the field of liberal arts, the quantity and quality of resources available to liberal arts students in various universities are basically equal. This suggests that the use of these two technological tools could help universities narrow a range of serious inequalities caused by uneven educational classes and geographical disparities. However, for the three regions, all universities in the developed regions, at any level, are significantly better than those in the other regions in the number of traditional paper archives and technological tools. That is to say, with the help of science and technology, the differences for universities in developed regions can be largely ignored, but the gap between universities in developed regions and other regions, west and middle of China, has not been narrowed by the use of technological tools. Therefore, this paper argues that the government should invest more in big data and multimedia in universities in the middle and western regions, which will help realize the relative fairness of education in all regions and at all levels with the minimum cost.

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Correspondence to Zongye Jiao .

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Jiao, Z., Shao, S., Wang, Y. (2021). An Approach to Observing China’s Higher Educational Equity: The Application of Multimedia and Database in Liberal Arts Education After Lockdown. In: MacIntyre, J., Zhao, J., Ma, X. (eds) The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy. SPIOT 2020. Advances in Intelligent Systems and Computing, vol 1282. Springer, Cham. https://doi.org/10.1007/978-3-030-62743-0_120

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