It is reported that the Blue Book of Big Data Development in Basic Education in China (20 15) was jointly published by the "Mobile Learning" Ministry of Education of Beijing Normal University-China Mobile Joint Laboratory, China Education Technology Association and Jiangsu Education Informatization Engineering Technology Research Center, and was exclusively supported by Beijing Tuoside Technology Co., Ltd.
Yu Shengquan, director of China Mobile Joint Laboratory, the "Mobile Learning" Ministry of Education of Beijing Normal University, pointed out that data will gradually replace scarce assets such as land, labor and capital, and become the most important core assets of society and enterprises. At the same time, data will also change human cognitive style and cultural ecology. "The thinking mode of man-machine combination will become the basic way for modern people to know the world, and the era of survival with smart devices has arrived." In the future, big data will evolve into a social culture of "everyone produces data, everyone shares data, and everyone manages data".
Yu Shengquan said that as the most important asset of education and schools, the focus of future education data collection will shift to unstructured and procedural data. "The collection of educational big data should be full sample and real-time data". The education department should also strengthen the integration and correlation analysis of education big data and big data in other fields (medical care, transportation, economy, social security, etc.). ) further enhance the scientific nature of educational decision-making. "
Yang Xianmin, an associate professor at Jiangsu Normal University and one of the leaders of the Blue Book, said that education big data does not simply refer to its large quantity, but more importantly, its "value". The core value of education big data is analysis and prediction. He said that compared with big data in other fields, big data in education is highly complex, and its application also needs a high degree of creativity. He believes that education is an ultra-complex system, and there is no clear and fixed analysis process and analysis method for big data in the field of education. The analysis of educational big data should not only comprehensively use traditional data analysis methods and tools, but also rationally adopt new methods and tools specifically for big data processing. "Strictly speaking, the analytical model of educational big data is not designed, but discovered." .
This blue book combs the policy progress of global education big data, interprets the connotation and uniqueness of education big data, analyzes the source and structure of education big data, introduces the commonly used education data collection technologies of 13 in four categories, and puts forward seven typical education data analysis models. By collecting and sorting out 49 typical application cases, five application modes of big data in basic education are extracted, and the ideas of educational data network construction at national, regional and school levels are discussed, as well as the existing problems and basic solutions of educational big data management.
In the subsequent Education Big Data Summit Forum, the participating experts discussed the deep integration of education and big data. Experts pointed out that it was the first year of education big data in China, and the government, enterprises, schools, researchers and the public began to pay attention to education big data. At the same time, China's education big data research and practice as a whole is still in the initial exploration stage, and there is no mature experience and model abroad to learn from. It is hoped that through the five-party alliance of "politics, production, learning, research and use", we will jointly help the development of big data in basic education in China, and jointly realize that education in China will lead the world.
Four experts, including Yu Shengquan, Gu Xiaoqing, Fang and Wei Shunping, gave speeches on the education reform in the era of big data, improving teaching practice with learning analysis, mobile learning and education big data research for basic education, and the construction and application of data-driven evaluation model for large-scale online education implementation.